zana mozaffari

Climate change can occur once in a thousand years. However, recent abrupt and severe climate shifts have emerged as a significant concern within societies and a substantial environmental challenge. Escalating temperature, polar ice melting, global sea level rise, and shifting weather patterns, all stem from climate change. One of the pathways towards achieving sustainable development involves the advancement of the agricultural sector, a vital economic segment. Progress in almost all sectors of economy, even the industry sector is closely correlated to growth in agriculture. Looking at the experiences of leading countries in agricultural production, the utilization of capital equipment across various agricultural activities has proven to enhance the productivity of factors like land, labor, and management. This, in turn, results in decreased production costs, increased investment returns, surplus domestic supply, and expanded agricultural product exports. The world confronts a climate change crisis due to widespread and damaging human pursuits aimed at resource acquisition. The repercussions of climate change, including rising sea levels, global warming, floods, droughts, and landslides, pose substantial threats to human existence. Among economic sectors, agriculture holds a particularly critical role in ensuring the sustenance of human populations. Yet, climate change places the agricultural sector under a severe risk, jeopardizing its capacity to provide food for humanity. Hence, the principal objective of this current study is to explore the impact of two climatic variables i.e. climate change risk and climate change performance index, on the added value of the agricultural sector across 54 member countries of the climate change performance index. These countries are categorized into three groups: strong performance (16 countries), moderate performance (28 countries), and poor performance (10 countries). The study period spans from 2010 to 2020, and the quantile regression method is employed on panel data to conduct the analysis.

The general definition of quantile regression states that if the linear regression model is assumed as the following equation, we have:yi=ˊxiβτ+uτi.  0<τ<1 (1)Quantτ(yi|xi)=xiβτ  (2) Equation (2) shows the τth conditional quantile function of the y distribution under the condition of random variables x in which the following condition holds:Quantτ(uτi|xi)=0      (3)In the quantile regression structure, the effect of observable features on the conditional distribution is estimated through the process of minimizing the absolute value of the error element. To estimate the model coefficients, the absolute value of the errors with appropriate weighting is used:Min Σyi≥ˊxiβ τyi- ˊxiβ+Σyi<ˊxiβ 1-τyi- ˊxiβ  (4)As mentioned, quantile regression is resistant to outliers. However, this method is not intended to recognize the heterogeneity of a country. In this research, the quantile panel regression method with fixed effects is used, which makes it possible to estimate the effects ofconditional heterogeneous covariance of inflation rate stimuli, thus controlling invisible individual heterogeneities. A suitable method has been suggested by Koner (2004) for solving such problems. He considers invisible fixed effects as parameters that are jointly estimated with the effects of auxiliary variables for different quantiles. The unique feature of this method is that it introduces a penalty term in the minimization to address the computational problem of a set of parameters; The parameters are calculated as follows:min(α.β)Σk=1KΣt=1TΣi=1N wkρτkyit-αi-xitTβτk+λΣiNαi  (6)In equation (6), i represents the number of countries (N), T represents the index for the number of observations of each country, K represents the quantile index, x is the matrix of explanatory variables, and ρτk is the quantile loss function. Also, Wk represents the relative weight for the kth quantile. λ is an adjustment parameter that reduces individual effects to zero to improve the performance of β estimates. If λ tends to zero, the penalty term is eliminated and a conventional fixed effects estimator is obtained. Whereas if λ tends to infinity, an estimate of the model is obtained without fixed effects. In this research, λ = 1 (Damette and Delacote, 2012).

The results of this research show that both climate change performance index variables and climate change risk have a positive and significant effect on the added value of the agricultural sector in all three functional groups and all deciles. This means that an increase in climate change performance and an increase in climate change risk, both of which represent the improvement of climate conditions in a country, which have a positive effect on the added value of the agricultural sector.

Nowadays, climate change crisis has become one of the most critical challenges facing humanity in the present century. Scientists and researchers attribute the main cause of climate change to destructive human activities aimed at obtaining more resources to meet their needs and desires. Global warming, rising sea and ocean levels, landslides, floods, and droughts are just some of the consequences related to the climate change crisis.Within this context, agricultural sector, as one of the most important economic sectors for providing human food, is under the influence of climate change and could seriously endanger the future of humanity due to food resource scarcity. The main objective of this researchis to examine the impact of two climate variables, namely climate change risk and climate change performance index, on the value added by the agricultural sector in 54 countries categorized into three groups: strong performance (16 countries), moderate performance (28 countries), and poor performance (10 countries) during the period from 2010 to 2020, using a multiple regression method on tabular data.The results of this study indicate that both climate change performance index and climate change risk have a positive and significant impact on the value added by the agricultural sector in all three performance groups and throughout all decades. This means that increasing climate change performance and climate change risk, both of which signify improved climate conditions in a country, positively affect the value added by the agricultural sector.

In recent years, the COVID-19 pandemic has imposed considerable harm on economies globally. This situation has prompted researchers to thoroughly examine the issue from different perspectives and view bolstering the resilience of countries as the most effective strategy to address the vulnerabilities. Such efforts can mitigate the harmful effects and negative outcomes of the disease. The susceptibility of economic systems to the shock of the COVID-19 pandemic varies, with some being significantly more vulnerable to its effects. In this context, the greater a country's ability to mobilize its resources against such risks, the more resilient it becomes. Hence, economic resilience is regarded as a comprehensive approach to enhancing the existing capacities and reducing economic vulnerability in the face of various crises and environmental threats. Moreover, economic resilience is shaped by the implementation of economic policies and has an accumulative nature. Individuals and different schools of thought have focused on the concept of resilience within the economic realm, structuring their studies accordingly. Economic resilience emerges as a pervasive criterion in the literature on economic stabilization.In the economic discourse, particular emphasis is placed on assessing the composite index of economic resilience against external shocks, aiming to quantify this notion. This study sets out to investigate how the COVID-19 pandemic has impacted the economic resilience of countries across high, medium and low levels of income. Addressing 150 nations, this research employs the PSTR model over the timeframe spanning 2020 to 2021. The Economic Resilience Index is computed utilizing the Bruegel method for this inquiry. 

The PSTAR (Panel Smooth Transition Autoregressive) model is a powerful tool utilized to analyze nonlinearities and regime shifts in panel data. It extends traditional autoregressive models by allowing for smooth transitions between different states or regimes, thereby capturing complex dynamics in economic time series data.The PSTAR model assumes that the relationship between variables evolves smoothly over time and across different regimes. It is particularly useful in capturing threshold effects, where the impacts of one or more variables on the dependent variable change abruptly beyond a certain threshold level. This is achieved through the specification of transition functions, which determine how the model transitions occur between different regimes based on the values of certain threshold variables. The PSTAR model estimation involves several steps, including the specification of transition functions, estimation of model parameters using maximum likelihood or other suitable methods, and diagnostic checks to assess the model's goodness-of-fit and validity of assumptions. Additionally, we conduct robustness checks and sensitivity analyses to ensure the reliability of our findings.

This study reveals a significant negative impact of the COVID-19 pandemic on the economic resilience of countries across different income levels. Employing a two-regime model, the analysis demonstrates a nonlinear correlation among the variables, suggesting varied adjustment speeds among economic regimes. Specifically, countries with high incomes experience a moderate pace of adjustment, those with moderate incomes adjust slowly, and low-income countries adjust rapidly. This suggests that economic changes in response to the pandemic occur at different speeds across income groups.The COVID-19 pandemic has induced structural and performance alterations in economies worldwide, including increased unemployment, reduced growth, and shifts in consumption and investment patterns. These changes, coupled with challenges such as quarantine restrictions and decreased incomes, have led to considerable adjustments in economic dynamics, varying in speed from one state to another.Overall, economic changes during the pandemic are of significant diversity across countries, affecting consumption and production behaviors, policy frameworks, and international relations. Threshold values, representing critical points of pandemic severity, determine whether economic variables behave linearly or nonlinearly. When the pandemic's impact surpasses these thresholds, economic variables begin shifting to nonlinear regimes.Estimations from the smooth panel regression approach indicate a notable negative effect of the pandemic on economic vulnerability, affecting countries with high, medium, and low incomes alike. Per capita GDP, remittances, and foreign direct investment emerge as key influencers of economic resilience. While per capita GDP decreases during the pandemic for high and medium-income countries, it increases in the second threshold regime and boosts economic resilience. Remittances positively impact economic resilience in high-income countries but negatively affect medium and low-income ones initially, transitioning to positive effects after crossing the threshold. Foreign direct investment initially reduces economic resilience across all income levels but becomes positive and significant in high-income countries after crossing the threshold.In summary, the study underscores the complex interplay between the COVID-19 pandemic and economic resilience, emphasizing the need for tailored policy responses to mitigate its adverse effects across different income groups.Conclusions and Policy Implications: Although the public health system is evolving, the world remains susceptible to shocks caused by contagious diseases that present fundamental challenges to economies. The vulnerability resulting from the COVID-19 pandemic has spurred countries to allocate more funds to ensure economic security and global health. Nations across the globe are working to reduce the economic impact of the COVID-19 crisis through well-balanced policies, seeking to mitigate risks to the society as much as possible. To tackle the COVID-19 crisis, extensive actions are being taken, including sharing knowledge and experiences regarding strategies, policies and improvement plans, examining common responses, and coordinating actions to enhance resilience and effective recovery preparedness. Strengthening communication among policymakers, development planners, and relevant stakeholders, establishing strong multilateral institutions with political support for global health shock coordination, creating fast financial procurement mechanisms for essential goods, increasing investment in disease surveillance at global and regional levels, developing regional production capacities for vaccines, tests and medicines, sharing medical technology and knowledge, and transferring medical technologies are the measures being taken. Additionally, strategic approaches to national improvement, including economic and financial measures, effective financial procurement design, and financial resource mobilization efforts to mitigate the effects of COVID-19, are being pursued. These measures aim to enhance the resilience and sustainability of public health systems and foster systematic research and development for the preparation of effective and safe vaccines against any virus. Therefore, based on the findings of this research and similar studies, policymakers need to focus on strengthening the economic resilience of countries, in addition to implementing other policies aimed at reducing the spread of and preventing the COVID-19 disease. These actions may include policies to strengthen healthcare infrastructure, develop economic and social crisis response plans, and encourage the development of human resources needed to combat the disease and its economic impacts.

One of the most important challenges of societies in the last few decades is the destructive and repeated human activities to destroy the environment. The ecological footprint is the latest index measuring the extent of individuals’ influence and engagement in addressing the issue of climate change as well as their negative impact on the environment. The economic policies adopted by societies have a significant effect on the process of ecological footprint. Monetary and Fiscal Policies are among the most important policies that, in addition to affecting the productive and economic activities of society, also affect the environment. Therefore, the main purpose of this study is to investigate the effect of monetary and Fiscal policies on the ecological footprint in Iran during the period from 1980 to 2021 and using the structural vector autoregression method. “Liquidity and the government’s final consumption expenditures have been employed as monetary and fiscal policy tools, while economic growth, urbanization, and foreign direct investment have been utilized as control variables.” The results of this research show that foreign direct investment and urbanization have a negative and significant impact on the ecological footprint. Also, economic growth, monetary policy, Fiscal policy and the dependent variable itself have a positive and significant effect on the ecological footprint and lead to an increase in the ecological footprint during the mentioned time period. On the other hand, fiscal policy impulses in the long term have resulted in a rise in environmental degradation in Iran. The results of variance analysis in the 10th period show that monetary policy explains 6.70% and Fiscal policy 24.06% of changes in the dependent variable.

The present paper aims to estimate the level of social capital and its trend in Iran and determine the main components and variables of social capital in Iran from 1988-2019. For this purpose, by analyzing the theoretical and empirical foundations of social capital, three main components (crime-participation-awareness) and the associated social capital were considered for calculating the social capital index. Then, using the Mamdani fuzzy inference system and a fuzzy approach, the social capital index was estimated. A review of the trend of this index indicates that in the period under study until 2001, there has been significant growth so, in 2001, this index was estimated to be 0.67. However, from 2001 to 2019, there was a downward trend with different fluctuations, so in 2018, this index was examined at its lowest level during the period under study. One of the reasons for the decrease in social capital could be the increase in the crime rate due to the growing trend of migration from rural to urban areas. Furthermore, to more accurately evaluate the estimation of the social capital index, based on the methods of previous studies, the estimation of social capital was performed using the Dagum method. The results of this method showed that according to the status of input variables and determinants of social capital, fuzzy estimation has provided a more accurate and appropriate estimate of social capital.

The research looks at how the COVID-19 pandemic affected vulnerability and economic strength in 150 countries between 2020 and 2021. By using the Panel Smooth Transition Regression model, it discovered a complex connection between different factors. The pandemic made countries more vulnerable and weakened their economic resilience. This highlights the need for nations to boost their strength by taking steps such as broadening their economies, investing in healthcare, creating support programs, maintaining trade, and building stronger economic defenses against both pandemic-related vulnerabilities and natural disasters. Ultimately, the article stresses the importance of countries reinforcing their resilience through strong policies and actions that cover different aspects of their economies and healthcare systems, addressing the challenges brought on by the pandemic and potential future crises.

Nowadays, good governance is recognized as a fundamental concept that holds significant importance at every level of organizations and societies. This type of governance, which focuses on transparency, justice, participation, and efficiency, profoundly impacts performance and development. Through transparent and accountable processes, good governance strengthens public trust and contributes to sustainable development. On the other hand, unemployment, as an economic and social phenomenon, is of notable importance. Therefore, the primary aim of this research is to examine the impact of good governance on the unemployment rate in Iran during the period from 1990 to 2021 using the quantile regression method. To achieve the research objective, variables such as foreign direct investment, per capita GDP, the real effective exchange rate, and the inflation rate have been used as control variables. The results of this study indicate that the good governance index has a negative and significant effect on the unemployment rate across all unemployment rate deciles. Per capita GDP and foreign direct investment also have a negative and significant impact on the unemployment rate across all deciles. Meanwhile, the inflation rate and the real effective exchange rate have a negative and significant effect on the unemployment rate from the first to the fifth decile, and a positive and significant effect from the sixth to the ninth decile.

The Covid-19 pandemic, as one of the recent world crises, has brought costs to the economies, which has drawn the attention of researchers and politicians to the concept of economic vulnerability in the form of a warning index to evaluate this external shock. The main aim of this study is to investigate the impact of the COVID-19 pandemic on economic vulnerability in high, medium, and low-income levels countries. This study was conducted for 150 countries using the Panel Smooth Transition Regression (PSTR) approach over 2020-2021. In this regard, the Briguglio method was used to calculate the Economic Vulnerability Index. The results of this research indicate that the COVID-19 pandemic has had a positive and significant effect on the economic vulnerability of countries. The linear test results confirm the non-linear relationship between the variables. Moreover, by considering a transfer function with a threshold parameter (the level of COVID-19 morbidity and mortality), a two-regime model is presented to specify the non-linear relationship between the pattern variables for three groups of high, medium, and low-income countries. The slope parameter (transfer rate) for these three groups of countries is 5.9876, 6.1569, and 3.9987, respectively. The model estimation results show that in both linear and non-linear regimes, COVID-19 has a positive impact on the economic vulnerability of countries with high, medium, and low incomes, meaning that an increase in the COVID-19 pandemic has led to a decrease in the economic vulnerability of these groups of countries.

Through extensive research and data collection, a sample of 150 countries for the period 2020-2021 has been selected. The primary criterion for selecting countries and the period is the availability of data. The research database includes sources such as the World Bank, the International Monetary Fund, and the United Nations Development Organization. The dependent variables in this study are the Vulnerability Index. The Vulnerability Index is constructed based on the Briguglio method using four components: 1) Trade openness 2) Export concentration 3) Dependency on strategic imports, and 4) Exposure to natural disasters. Other variables included in the model are the number of COVID-19 deaths, per capita gross domestic product (GDP), foreign direct investment, and remittances as a percentage of GDP, which have been collected from the World Bank and other reliable sources. This study used Panel Smooth Transition Regression (PSTR) approach. PSTR is a statistical model that is commonly used to analyze the non-linear relationships between economic variables. This model is particularly useful for investigating the behavior of variables that exhibit non-linear patterns or changes in their behavior over time. PSTR is a flexible model that can be used to capture the complex relationships between different variables, making it a popular choice in various fields, such as economics, finance, and social sciences. The PSTR model is an extension of the Smooth Transition Regression (STR) model, which is a non-linear regression model that allows for the specification of the transition function between two different regimes. In the PSTR model, the transition function is extended to include panel data, which allows for the analysis of the non-linear relationships between variables across multiple units, such as countries or firms, over time. PSTR is a powerful tool for analyzing the impact of various economic factors on different regions or countries. For example, it can be used to investigate whether the impact of a particular economic policy or event is uniform across different countries or regions, or whether it varies depending on the level of economic development or other relevant factors. Additionally, PSTR can be applied to different types of data, including cross-sectional, time series, and panel data, making it a versatile tool for analyzing a wide range of economic phenomena.

the vulnerability model indicates that the slope parameter, which represents the speed of transition from one regime to another, is equal to 1191.414, and the regime change location is 435.6, with the logarithm of its anti-value being 2213094. Therefore, as long as the COVID-19 pandemic (mortality) value is less than the anti-logarithm values, the variables will behave according to the first regime. If the value of the COVID-19 pandemic exceeds the anti-logarithm values, the variables will follow the second regime. Based on the results of the two regimes, it is evident that the COVID-19 pandemic variable has had a positive and significant impact, both linear and nonlinear on countries. This means that the increase in the COVID-19 pandemic has led to an increase in the economic vulnerability of countries. In other studies, such as Brzyska & Szamrej (2021), Marti (2021), and Puertas, it has been demonstrated that the COVID-19 pandemic has had a positive and significant effect on the vulnerability of countries in the European Union, which mostly includes high-income countries.

This paper examines the impact of the COVID-19 pandemic on economic vulnerability in 150 countries during 2020-2021. The results obtained from the Panel Smooth Transition Regression (PSTR) model confirm a nonlinear relationship between the variables and the presence of two threshold regimes with a threshold for economic vulnerability and model. It also indicates that the COVID-19 pandemic has a positive effect on vulnerability. This means that an increase in the COVID-19 pandemic has led to an increase in vulnerability and a decrease in economic resilience in these countries.

Employment, as one of the factors of development, has a significant impact on economic growth, poverty reduction, and social welfare. Foreign direct investment flows in the world in the last two decades have had a noticeable impact on the labor market in developed and developing economies. The impact of foreign direct investment on employment levels can vary depending on the economic conditions and status of each country, as well as the type and amount of inbound investments. This current article evaluates the impact of foreign direct investment on employment levels during the period 2019-2000 in 6 selected countries in the Persian Gulf region, including Iran, Saudi Arabia, Qatar, Kuwait, Bahrain, and Iraq, using panel data methodology. The results show that foreign direct investment has a positive and significant impact on employment. Additionally, gross domestic product has a positive and significant impact on employment. Oil revenues, human capital, and institutional quality have a negative and significant impact on employment. Based on the results of the article, attracting FDI can be proposed as a strategy to increase employment levels in these countries. In other words, foreign direct investment and gross domestic product can significantly contribute to increasing employment and improving the economic and social conditions of countries in the Persian Gulf region.

Investigating and Comparing the Effects of Corporate Income Taxes and Government Expenditures on Unemployment Rates in Iranian ProvincesAbstract:Unemployment is one of the most significant economic and social issues in the country, with extensive implications for people's lives and economic development. Reducing the unemployment rate through various policies, including changes in government expenditures and tax rates, is a matter of concern. Given the importance of the issue of unemployment at the provincial level in Iran and the necessity of identifying the factors affecting it, this study seeks to investigate and compare the effects of corporate income taxes and government expenditures on the unemployment rates in Iranian provinces during the years 2008-2021. The model under study is dynamic, as the unemployment variable is influenced by its past values, thus creating autoregressive conditions. Accordingly, the econometric method used in this study is generalized method of moments (GMM). The results showed that corporate income taxes, the unemployment rate in the previous period, and urbanization have a positive impact on unemployment. On the other hand, economic growth variables and the government expenditure index have a significant negative effect on unemployment. Considering that the coefficient of corporate income taxes is greater than the estimated coefficient of government expenditures, corporate taxes have a more significant impact on the unemployment rate than government expenditures. Based on the results, it is suggested that the policy-making process should prioritize reducing corporate income taxes and allocating government expenditures towards job creation and business development. 

Nowadays, as the most diverse, the tourism industry is the most vital source of income and job opportunities for many countries in the world. Iran has the potential to attract tourists due to its rich historical, geographical, cultural, and natural attractions. Iran has the potential to attract tourists due to its rich historical, geographical, cultural, and natural attractions. Knowing the factors affecting the tourism industry is critical; however, identifying tourism obstacles can be more vital. In today's world, various risks affect the economy of countries. On the other hand, climate change has become the most crucial concern of humankind in this century. This paper uses the quantile regression method to investigate the impact of country risk, including economic risk, political risk, financial risk, and ecological footprint, on tourism demand in Iran from 1989 to 2020. The results of this research illustrate that country risk and its components have a negative and significant impact on tourism demand in Iran; this can demonstrate the importance of risk and uncertainty in Iran's economy. Moreover, the ecological footprint as a proxy of climate change negatively and significantly affects tourism demand in Iran. According to the research results, it is suggested that policymakers adjust the policies to reduce the country's risk and increase the confidence of foreign tourists to travel to Iran. On the other hand, paying attention to the climate change crisis should become more critical for the government and society; otherwise, this crisis can negatively affect tourist attractions in Iran.

In general, the lack of balance in income distribution can have a negative impact on the environment in two different ways. On the one hand, individuals with higher incomes may excessively use natural resources and introduce more pollutants into the environment. On the other hand, individuals with lower incomes may have insufficient exploitation of natural resources due to lack of resources, which may lead to environmental degradation. Therefore, inadequate incomedistribution can lead to an inappropriate ecological footprint. This article examines the effect of income inequality on the ecological footprint in Iran using the GMM time series method over the period of 1990-2020.

For model estimation, the GMM time series econometric method was used because the model used in this study is dynamic and satisfies the generalized moment conditions. In fact, GMM is used for time series models that are linearand meet the conditions of generalized moments and ancillary properties. The use of this method for estimating models can have many advantages.

The estimation results showed that the model's statistical indicators are in a satisfactory state. The null hypothesis of the Sargan test cannot be rejected, indicating that the instrumental variables used in the estimation model are valid. Given the logarithmic nature of the model, all the estimation coefficients reflect elasticity. The ecological footprint of the previous period had a positive and significant impact on the ecological footprint of the current period. Income inequality had a positive and significant impact on the ecological footprint, while the use of renewable energy had a negative and significant impact.

Ecological footprint is a comprehensive indicator of human pressures on the environment. Income inequality in the economy can have various effects on environmental quality, including its impact on social demand. Income inequality can affect economic and environmental behaviors by changing consumer preferences. Previous research in the Iranian economy has used greenhouse gas emissions such as carbon dioxide as a proxy for environmental destruction. Therefore, this study examines the effect of income inequality on the ecological footprint of Iran during the period of 1990-2020. Based on the results, environmental quality is influenced by previous period values, and a dynamic GMM time series model is used to examine this issue. The estimation results show that income inequality has a positive and significant impact on the ecological footprint. Income inequality distribution in a society can have profound effects on ecological footprint. For example, individuals with high incomes can make more purchases, which can lead to increased production, resource use, and environmental destruction.Income inequality can also lead to increased class and social diversity, and individuals with lower incomes may predominantly live in poorer and more vulnerable areas, which are likely to have poorer environmental conditions. Additionally, income inequality can create imbalanced decision-making in various areas, including environmental protection, due to unequal distribution of resources and power in society.Overall, income inequality can lead to increased environmental destruction, reduced natural resources, and increased income inequality.

Both on micro and macro scales, the exchange rate and its dynamics play a pivotal role as influential variables in the economy. They can affect most economic, social, and cultural variables through various channels and change their behaviors. Given the importance of the exchange rate as a key variable in the Iranian economy, extensive studies have been conducted on the exchange rate and the associated concerns regarding exchange rate volatility. The rate affects many of the government's economic policies. In addition, since housing costs constitute the main household expenses in Iran, problems and limitations in providing housing can have a significant impact on the spread of social dissatisfaction. The purpose of this study is to investigate the effect of exchange rate instability on the housing price index in Iran. Therefore, analyzing the relationship between the exchange rate market and the housing market can help policymakers and economic decision-makers adopt appropriate policies in the foreign exchange market and housing sector.

In this study, the GARCH method was initially used to estimate the volatility of the exchange rate. Next, to investigate the effect of exchange rate instability on the housing price index from 1991 to 2020, the GMM time-series method was employed. Due to the dynamic nature of the model in this study and the need to establish conditions for generalized moment estimators, the use of this method seems appropriate. The GMM method is used for linear time-series models to ensure the conditions of moment estimators and incidental properties.

The results showed that the volatility of the exchange rate has a negative and significant effect on housing prices. Also, it was found that the interruption of the housing price index, oil revenues, inflation rate, liquidity, and urbanization all exert a notable and positive influence on housing prices. Additionally, the study's findings revealed a significant and adverse effect of variables such as interest rate, gold market return, and stock market return on housing prices within Iran's economy. The results of the tests used demonstrated that the estimation model is in a suitable condition in terms of statistical indicators.

Exchange rate volatility is of the parameters affecting housing prices. Accordingly, this study concludes that exchange rate volatility harms the housing price index in Iran. It is recommended that the central bank adopt appropriate currency policies to reduce volatility in the currency market and its effect on parallel markets, including the housing market. The results of estimating the coefficients of control variables are consistent and in line with the theoretical and empirical literature. Variables such as inflation rate, liquidity, and urbanization have a positive and significant effect on the housing price index. With an increase in liquidity, the housing price index in the Iranian economy increases, indicating that expansionary monetary policies have significant effects on the housing market in Iran.

Nowadays, environmental problems, especially air pollution, are one of the major issues in the world's metropolises with increasing its dimensions and side effects. Humans are one of the main sources of air pollution. The age structure of the population is an important indicator in the progress of societies. It can be said that one of the effective factors in economic growth and long-term socio-economic development plans is the age structure of the population (youth or aging population). In working-age (provided that the labor market has the capacity to absorb more workforce in activities), increasing labor supply leads to economic growth.In general, the age structure of population is important because economic activities and energy consumption vary by age or stage of life. On the other hand, the age of household head is related to household size (people over 65 usually have smaller households than middle-aged people). Studies conducted in Iran have mostly focused on examining Kuznets' environmental hypothesis, the impact of income and population changes on carbon dioxide emissions, and so far have not examined the effect of aging on carbon dioxide emissions. Therefore, this article examines the effect of aging on air pollution in Iran. This article uses the GMM to investigate the dynamic effect of population aging on air pollution during 1981-2020.

To collect information for this study, a documentary method was used. The research was conducted based on annual data from 1982 to 2020 in Iran's economy. EViews software was used to estimate the model. It should be noted that data related to research variables were extracted from various sources such as the Central Bank of Iran Statistical Center of Iran, and Energy Balance Sheets.To estimate the model, a time series econometric method called GMM was used because the model used in this study is dynamic and satisfies generalized moment conditions. In fact, GMM is used for time series models that are linear and also provide generalized moment conditions and instrumental variable properties. This method have many advantages.

In this study, the impact of population aging on air pollution was investigated using the GMM in Iran during the period of 1981-2020. The estimation of the model indicates that increasing the age of the population and the transition of the age structure of the population to the aging stage has a negative effect on air pollution. As it is predicted, Iran will face the problem of population aging in the next few years. According to the results, with increasing population, it can be expected that air pollution and environmental degradation will decrease. Due to the negative coefficient of the per capita income squared, the Kuznets environmental hypothesis is confirmed by considering the age structure of the population. In addition, urbanization, industrialization, trade openness, pollution of the previous period have positive and significant effects on air pollution.The results indicate an inverted U-shaped EKC pattern between environmental degradation (pollution emissions) and per capita income (economic growth); therefore, it can be concluded that environmental degradation initially increases with increasing per capita income in a country, but after reaching a certain level of economic growth, environmental degradation stops and then decreases. Therefore, the results confirm the Kuznets environmental hypothesis for Iran. Based on this, it is recommended that the government design its plans with environmental considerations, especially air pollution. The results indicate that population aging has a significant negative effect on air pollution emissions. Population aging is detrimental to a country's economy and although it is inevitable for any country, policies to increase the number of elderly people in the population mix cannot be formulated and implemented even though it reduces air pollution levels. However, the harm caused by population aging outweighs this benefit. Of course, recently topics such as "active aging" have been raised to deal with population aging logically and should be on policymakers' agenda given the transition of age structure and movement towards aging in future years.

Based on the evidence of Iran's population age distribution during the period under study, it can be said that in future decades, older individuals will make up a higher percentage of the total population. This will lead to a reduction in carbon dioxide pollution automatically without government intervention or any other actions. The results of this study also show that urbanization and industrialization have positive impacts on air pollution. This result indicates that with the increase in the number of industrial enterprises and the trend towards urbanization in Iran, air pollution has increased. The reason for this is that most industries in Iran are energy-intensive and use fossil fuels. Another reason is the outdated technology with low efficiency in production. Other research findings show that pollution from previous periods and trade liberalization have positive effects on air pollution. The policy of economic liberalization by creating division of labor and using advantages, increasing capacity utilization in industries, increasing capital formation rates, changing technology, and creating competition in international markets lead to higher productivity levels for all production factors at a higher level.

Economic growth is one of the goals that every economy pursues, and the reason for this is the achievement of many benefits and advantages that are realized in the process of growth. Policy makers in every country seek to achieve a higher growth rate in their country.
Among the types of assets, housing is considered one of the most important socio-economic components in a country. It is also argued that in the new theories, the development of the housing sector is one of the drivers of economic growth, so in this article, the impact of investment in housing on the economic growth of Iran's provinces during the years 2006-2019 has been examined.

According to the point of view of macroeconomics, considering that between 21 and 24% of the country's GDP is related to the housing sector (during the construction and operation period), the housing sector is considered as the driving engine of economic growth and the factor that strengthens it in the country. From the point of view of consumption and investment, housing also plays a significant role in national accounts.
In general, there are three different views on the relationship between investment in real estate and economic growth. The first point of view emphasizes that investment in real estate affects economic growth. The second point of view shows that economic growth affects investment in real estate. According to the third perspective, there is a mutual causal relationship between investment in real estate and economic growth.

To estimate the research model, the econometric method of generalized moments of GMM has been used. The important feature of this estimation method is that there is no need to know the exact distribution of disturbance sentences. The main assumption of this method is based on the fact that disturbance terms are not correlated with instrumental variables. The method of generalized moments, by choosing the correct instrumental variable, by applying a weight matrix, can create a compatibility estimator for the conditions of heterogeneity and unknown autocorrelations.

The results show that the economic growth coefficient of the previous period was evaluated as 0.73. Based on the probability level corresponding to the t-statistic of this coefficient, it can be stated that the economic growth of the previous period had a positive and significant effect on the economic growth in the provinces of Iran. This result is consistent with economic theories and most previous studies such as Kazerooni et al (2018) and Mozaffari (2021). The variable coefficient of human capital has been evaluated as 0.16 and according to the probability level corresponding to its t-statistic, it can be stated that human capital has had a positive and significant effect on the economic growth of Iran's provinces. This result is in line with endogenous growth theories and previous empirical studies such as Kazerooni et al (2018) and Mozaffari (2021).
The size of the government also has a negative and significant effect on economic growth. So that the elasticity of the economic growth of Iran's provinces in relation to the government's current expenses is equal to -0.32.
The urbanization variable has a negative and significant effect on the economic growth of Iran's provinces. The negative sign of the variable coefficient of urbanization can be caused by the fact that the rate of urbanization has increased regardless of the process of providing the necessary infrastructure in the cities and increasing the expertise of the migrant workforce.

Investment in housing has a positive and significant effect on the economic growth of Iran's provinces during the research period. Which is compatible with the growth models and is in line with the studies conducted in the field of the effect of investment in real estate on economic growth, it is recommended that the government, in addition to paying special attention to capital expenditures in the field of housing; It has provided the necessary incentives and conditions for the investment of the private sector in the field of housing, so that the investment in this sector has increased, which on the one hand has increased the welfare and economic growth of the society, and on the other hand, it can reduce the lack of housing supply in this way, the Iranian market can be partially compensated. The effect of capital stock on the economic growth of Iran's provinces also has been positive and significant, which is in line with the existing theory in this field and previous studies, it is recommended that the government provide a mechanism and in the budgeting of the provinces. The discussion of the number of credits for the acquisition of capital assets should be given special attention in order to improve the economic growth of different regions of the country. Human capital and industrialization index have a positive and significant effect on the economic growth of Iran's provinces. Aggregation of industrial activities leads to growth by creating savings resulting from local aggregation. The effect of urbanization on the economic growth of Iran's provinces was negative and significant. Although the previous theory and studies show a positive and significant effect of urbanization rate on economic growth. Governments should pay attention to prevent the increase in the size of the government, because based on the results of this research, the economic growth has decreased with the increase in the size of the government, and the large involvement of the government in the economy can have negative consequences. This result is consistent with the results of most previous studies in developing countries.

Increasing people's participation in the capital market is one of the important discussions in the process of economic development. Housing as a capital good and stocks are important channels in the assets market. It is of high interest to investigate the influence of these markets on each other and how housing prices can influence them and interact with them. The current research is different in its contecnt and methodology than previous studies in the followimg ways: 1- Estimation of the asymmetric effects of housing prices on participation in the Tehran Stock Exchange. 2- Using the asymmetric quantile regression method to investigate the participation model in the Iranian stock market. The main aim of this research is to investigate the asymmetric effects of housing prices on the participation rate in the Iranian stock market. The method used is quantile regression as it allows to present the influence of independent variables in all parts of the distribution, especially in the beginning and end sequences, without violating the classical assumptions and outlier data in the estimation of coefficients. The variables presented in the model are: logarithm of stock market index, logarithm of gold price, logarithm of unofficial exchange rate, logarithm of housing price, logarithm of GDP per capita and interest rate. The time period of this research is 1991 to 2020. The results show that housing prices in different quantiles of participation in the stock market had negative and significant effects on stock market participation. The estimation results show that the absolute value of the currency elasticity of participation in the stock market is smaller than 1, with negative sign and significant. Among other results, the negative and significant effect of gold price and interest rate, mostly  in the upper deciles are worth noting. GDP per capita and stock market efficiency have had a positive and significant effect on participation in the stock market.

Investing in housing is one of the most common methods of investing in Iran. The housing and construction sector has a widespread relationship with other economic sectors. Housing annually attracts a large amount of liquidity in the country, so investing in this sector is more important in the process of growth. In this paper, first the relationship between investing in housing and economic growth is evaluated by the Granger causality test. The results of this test showed that there is a one-way causal relationship from investing in housing to economic growth. Then, using GMM method and time series data from 1981-2019, the impact of investing in housing on Iran's economic growth is studied. The results showed that investing in housing has a positive and significant effect on Iran's economic growth. The lag of variable “economic growth” has a positive effect on economic growth in later years. Also, other results indicate that human capital, capital stock, government expenditure index and industrialization index have positive and significant effects on Iran's economic growth. Based on the results, it can be suggested that the shortcomings and barriers to investment in the housing sector should be removed, and also incentives and facilities for investment in housing can be effective in increasing economic growth.

Human capital is a hidden variable. In different economic studies, various proxies have been used as a proxy for human capital, including the average literacy index, the number of graduates or the average number of years of schooling. This study will review the economic literature first, and then the three pillars of human capital index including education variables, skills and health will be analyzed for the Iranian economy. In addition, by using fuzzy approach and Mamdani Fuzzy Inference System, the human capital index in the Iranian economy during the 1981-2019 period will be estimated. The results of this calculation shows that during the period under study, the human capital index has continuously grown; in 1981, the index was estimated at 0.13 and 0.59 in 2019. On this basis, it can be stated that human capital in the Iranian economy during the 1981 to 2019 period has grown significantly. This accumulation of human capital can be seized in the production processes, leading to increase in production and productivity..

Water is one of the most important and vital factors in the life of living organisms. In this regard, prevention of water pollution is equally important and important. Water contaminating agents are very diverse and can pollute both groundwater and surface water. The main of this paper is to investigate the effect of social capital and human capital on water pollution in Iranian provinces during the period of 1396-1386. In this regard, using fuzzy logic of social capital was estimated in each province of the country and resulted showed large difference between provinces, then according to the dynamism of the model, the method of generalized moments was used. The results showed that social capital and human capital had a negative and significant effect on water pollution. Water contamination of the previous period, industrialization, economic growth and urbanization have had a positive and significant impact on water pollution in Iran's provinces. Also, the use of chemical fertilizer in agricultural land also has a positive and significant impact on water pollution.

The quality of manpower is one of the factors affecting the environmental degradation. According to studies, air pollution is affected by its past values, so a dynamic model should be used to study it. Accordingly, using the GMM method, the present study evaluates the impact of human capital on air pollution in Iran during 1981-2019. Human capital is a latent variable in economics and is often replaced by alternative proxies. In this paper, like most previous studies, the research model was firstly estimated using the average proxy of years of schooling (as an indicator of human capital), which resulted in statistical insignificance and theoretical inconsistency in the estimated coefficients. Based on theoretical foundations, it is argued that the human capital index in addition to the education component is influenced by other aspects such as skills and health. Therefore, using fuzzy logic, an indicator for human capital in the Iranian economy has been constructed, so that it includes three main aspects (education, skills and health) of human capital. The results of estimation of the air pollution model using the human capital index showed that improving the level of human capital had a negative effect on air pollution. Therefore, by increasing human capital and improving the quality of manpower, we can expect to reduce air pollution and environmental degradation. In addition, urbanization, industrialization, trade freedom, economic growth and pollution in the previous period had positive and significant effects on air pollution.

One of the factors affecting the industrialization of the economy is human capital. In many previous studies, education has been used as a traditional index of human capital, but in addition to education, human capital is also affected by other aspects such as skills and health. Given that industrialization is a dynamic process, the present study has investigated the effect of human capital on industrialization in Iran during 1981-2019 by using the Generalized Moment Method in the context of time series and also applying traditional and comprehensive indicators of human capital. In this regard, using fuzzy logic, the comprehensive index of human capital has been estimated by considering three aspects of education, skills and health. The results show the significant growth of human capital during the study period. The results of estimating the model using the traditional index of average years of education showed the insignificant and inconsistency of coefficients and the estimation results using the comprehensive index of human capital showed that the promotion of human capital level leads to increasing value added of industry as a percentage of GDP. Also, the effect of the first lag of industrialization, labor force participation, capital stock and technical progress has been evaluated positively and significantly.

The quality of human force is one of the effective factors on economic growth. Classical time series methods do not have the ability to estimate models that have moments conditions. Since economic growth is influenced by past values, we need a dynamic model and it should be estimated by dynamic econometric method. This study by GMM method in time series evaluate the effect of human capital on economic growth during 1981-2019.Human capital is a latent variable in economics and often is replaced by alternative proxies. Based on theoretical foundations, it is argued that the human capital index in addition to the education aspect is influenced by other aspects such as; skill and health. Therefore, in this study, by using fuzzy logic, an indicator for human capital in the Iranian economy has been made that considers three main aspects (education, skills and health) of human capital. The results of index estimation showed that the organizing of human capital has grown significantly. The results of estimating the economic growth model using the fuzzy human capital index showed that promoting the level of human capital increases economic growth. Other results indicate the positive effect of the variables such as; governmental expenditures, industrialization, physical capital and economic growth of the previous period on Iran's economic growth. Also, trade freedom and urbanization negatively impact on Iran's economic growth.

Makran beaches in the south-east of the country have been the focus of attention of our government and armed forces in recent years. Access to free waters and expanding trade with regional and trans-regional countries are areas of economic and developmental attention to these shores; from a defense and military point of view, Makran has many advantages that could present Iran as a trans-regional power. The presence of a strategic navy on the Makran coast is the most obvious reason for the fact that Iran's strategic horizons are expanding to use its capabilities to create a stronger naval force to increase its influence in open waters. The Islamic Republic of Iran, of course, understands the strategic importance of the West and East as highways, and is aware that it is in these areas that it is in possession of international oil pipelines. Iran's maritime strategy continues to evolve. The purpose of the present study is to present a model of economic development of the coasts of the coasts with social and security considerations in mind. For this purpose, Delphi method identified 4 main and key structures (economic, institutional, socio-cultural, strategic and geopolitical) that each of these structures include components that include all the components of using guidelines. Supreme leadership, development of transport infrastructure; utilization of local and local resources and resources; bringing informal institutions to the region with economic development goals and activating fisheries and aquaculture capacities in the Makran region. The degree is more important.

Achieving proper economic growth and understanding the factors affecting it are among the most important goals of macroeconomics. New studies show the high impact of health, education and food inflation on economic growth. The purpose of this paper is to investigate the effects of food inflation and health expenditures on Iranian economic growth during the years 1974-2015. In this regard, the simultaneous effect of food inflation and health expenditures on economic growth was evaluated by using ARDL method. The results showed that food inflation had a negative effect on economic growth but health expenditures had a positive and significant effect on economic growth. Also, the results showed that the literacy rate, capital and labor taking part had a positive and significant effect on economic growth. The error correction model factor represents a good modulation rate. Other results showed that the coefficients were stable.

Economic security is one of the most important aspects of national security. It is a basic pillar of the economies for achieving economic development and improving social welfare, too. The demographic changes may widely affect the development and trend of target variables of the economy. The identification of the effects of demographic changes on economic security can help policy-making and planning of economic development. The purpose of this paper is to investigate the effect of age structure of the population on economic security in Iran during the period 1981-2016. In this regard, the economic security index (ESI) is firstly estimated using fuzzy logic. The findings indicate the instability of this index. Then, the impact of demographic model of Iran along with other variables affecting Iran's economic security is evaluated using generalized method of moments (GMM). The results show that age structure of the population has a negative and significant effect on Iran's economic security. Population growth, capital stock, financial development, trade openness, and saving rates have positive and significant impacts on the ESI. However, the inflation rate has no significant effect on the ESI. Iran’s population is rapidly aging, and if the current trend continues, the limited economic resources should be assigned to meet the needs of the elderly instead of investing in productive sectors. Therefore, there is a need for planning to tackle this problem.

Environmental issues are related to social capital. Increasing human capital can reduce environmental pollution. The main of this paper is to investigate the effects of social capital and human capital on air pollution in Iranian provinces during the period of 2000-2015. In this regard, using fuzzy logic of social capital was estimated in each province of the country and resulted showed large difference between provinces. Then, considering the dynamism of the research model, the effects of social capital and human capital along with the control variables on air pollution emission were studied using GMM method. The results showed that social capital and human capital have a significant negative effect on air pollution in Iranian provinces. Therefore, the government can reduce the spread of air pollution by planning in the field; increasing expertise, improving the quality of manpower, raising awareness, reducing corruption, increasing people's participation, and reducing the gap between people and government. Other results of the study showed that economic growth, industrialization, energy intensity, urbanization, and past pollution contributed significantly to air pollution in Iran's provinces. However, squared per capita income variable have no significant effect on air pollution, so the Kuznets environmental hypothesis is not confirmed in Iran's provinces. Therefore, in order to establish this EKC, special attention should be paid to the role of variables of social capital and human capital.