This paper seeks to answer the question whether Macroeconomic Environment in the event of fiscal policies shocks will affect their efficiency. For this purpose, a two-stage approach and data during years 1357 to 1391 are used. First, fiscal policy shocks were recognized as the residuals from estimating of first-order autoregressive model for government spending and tax revenues. Next, the shocks were considered as explanatory variables in the specified function. In addition, narrative approach is used to demonstrate the effects of economic position on the efficiency of identified shocks have been used by traditional methods. Thus, the Macroeconomic Environment on the basis of the key Macroeconomic variables including money growth, inflation and unemployment to economic good and bad conditions is separated and composed using a combined dummy variable in the model. The results show the efficiency of government spending and tax shocks is dependent to the Macroeconomic Environment.

Iran's hand woven Carpet is one of the main sources of foreign exchange earningsand a the major export item, that due to its special position in world markets and also due to the need for decreasing the country's dependence on oil revenues; efforts to develop carpet export can cause economic growth and lead to escape from single-product economy, which is one of the main goals of Economic Development Programs. However, unfortunately in recent years, Iran's share of world markets has declined and this decline is influenced by many factors.So to identify effective factors in development of Iran's carpet export, we focused on Macro variables of markets to draw attention of policy and decision makers to this issue and to the necessity of revising relevant policies. Macro dimensions of globalmarkets Environment include 1) Economic, 2) social and cultural, 3) Political and legal and 4) Technical Environments and there are many indexes in each of these Environments. So through polling from Experts of this Art–Industry and using factor analysis method in SPSS software, we got to seven major effective indexes and chose them as hypotheses of this study. They are Income Distribution and Lifestyle in Target Market, Exchange Rate and Fiscal-Monetary Policies of the Country, Tariff & Nontariff Barriers, International Pressures, and Ecommerce Applications.Hypotheses were investigated by using nonparametric tests of one-variable median test and binomial or ratio test. According to the results of both tests, all the seven hypotheses are confirmed. And thus our variables are meaningfully effective on de10 velopment of Iran's carpet export.Besides for comparing mean ranking of effective variables, we used Friedman ranking test; and for paired test between the variables we used Wilcoxon nonparametric test and results of Wilcoxon test indicates that mean rank of international pressures is greater than the other variables.

Highlights:   - Investigates the correlation between Environmental factors and UHI intensity in the Isfahan metropolitan area over 10 years. - Utilizes MODIS Aqua & Terra data alongside Landsat 8 imagery for comprehensive UHI analysis. - Establishes a significant relationship between UHI and urban built density, vegetation, and water features. - Determines vegetation as the most influential factor in mitigating UHI compared to other elements. - Highlights the vital role of natural infrastructure in urban planning for UHI mitigation.   Introduction:   The Urban Heat Island (UHI) effect, characterized by a temperature increase in urban areas compared to their rural counterparts, presents considerable Environmental challenges, impacting public health, urban energy systems, and city sustainability. This phenomenon, fueled by rapid urbanization and industrialization, exacerbates heatwaves, posing risks to public health. Understanding the UHI effect is essential for developing responsive urban planning strategies both spatially and institutionally. This study, centered on Isfahan, Iran, explores the correlation between UHI intensity and Environmental factors, encompassing both built and natural attributes across five scenarios, including four seasons and one analyzing the ambient effect of the ZayandehRud river.   Theoretical Framework: The UHI phenomenon involves complex interactions among various urban and Environmental factors. The density of the built Environment contributes to UHI exacerbation through heat storage and anthropogenic heat discharge. Air pollution, especially with greenhouse effects, directly influences heat-trapping and UHI formation. Conversely, green infrastructure and water bodies offer UHI mitigation through cooling effects. This study integrates theoretical basics from urban planning, climatology, and sustainable development for an analysis of how both natural and built elements correlate with UHI intensity in the Isfahan metropolitan area.   Methodology: A mixed-method approach is adopted to address the multifaceted nature of Urban Heat Island (UHI) and its potentially correlated Environmental factors. Land Surface Temperature (LST) data, crucial for delineating UHI, were extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors onboard NASA's Aqua and Terra satellites, covering a decade from 2011 to 2021. This extended timeframe facilitates the exploration of UHI patterns across various seasons, examining specific impacts during both the flow and dry periods of the ZayandehRud river, as well as distinguishing LST differences between daytime and nighttime periods—unlike Landsat Satellite Images. Additionally, Landsat 8 images are utilized to process the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) for mapping green infrastructures and water bodies. Air Quality Index (AQI) data from the year 2020 supplements the study, allowing for an investigation into the relationship between air pollution and UHI. The analysis of all datasets employs the Pearson correlation coefficient to ascertain the nature and extent of correlation among UHI and the identified Environmental variables.   Results and Discussion: The findings reveal the persistent prevalence of Urban Heat Island (UHI) during nighttime across all scenarios in Isfahan. However, during daytime hours, the trend shifts, giving rise to cooler zones within the city borders, indicating the emergence of urban cold islands. A noteworthy revelation from the study is the significant exacerbation of UHI attributed to the density of the urban built Environment. Intriguingly, air pollution, though exerting a lesser impact on Land Surface Temperature (LST) compared to built density, still plays a role in elevating LST during daylight. The study underscores the pivotal role of urban green infrastructure and water bodies in mitigating heat islands. Among these elements, green spaces, particularly vegetation, emerge as highly influential, surpassing the cooling effects of both water bodies and polluted air. The seasonal variation in vegetation cover also influences UHI intensity, with reduced vegetative cover in colder, drier seasons contributing to heightened UHI effects. These spatial and temporal dynamics emphasize the intricate balance between UHI and Environmental factors, offering valuable insights for decision-makers. Such insights can guide targeted strategies in urban planning and design to address the challenges posed by UHI.   Conclusion: The study emphasizes the importance of considering UHI in urban planning, design, and sustainability discussions. Strategies include reducing built density and integrating green and blue infrastructures. Addressing air quality and vegetation cover in shaping urban thermal landscapes suggests comprehensive policies. Guarding against UHI through natural space preservation and innovative design solutions tailored to Isfahan's climate can enhance urban livability. Future work should quantify contributions of different elements for comprehensive UHI mitigation models. This Isfahan case study serves as a cornerstone for wider applications across similar cities, aiding in combatting global warming and UHI effectively.

This research examines different feature selection methods to enhance the predictive accuracy of Macroeconomic forecasting models, focusing on Iran’s economic indicators derived from World Bank data. Fourteen feature selection techniques were thoroughly compared, classified into Filter, Wrapper, Embedded, and Similarity-based categories. The evaluation utilized Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) metrics under a 10-fold cross-validation scheme. The findings highlight that Stepwise Selection, Tree-based approaches, and Similarity-based methods, especially those employing Hausdorff and Euclidean distances, consistently outperformed others with average MAE values of 32.03 for Stepwise Selection and 62.69 for Hausdorff Distance. Conversely, Recursive Feature Elimination and Variance Thresholding exhibited weaker results, yielding significantly higher average MAE scores. Similarity-based approaches achieved an average rank of 9.125 across datasets, demonstrating their robustness in managing high-dimensional Macroeconomic data. These outcomes underscore the value of integrating similarity measures with traditional feature selection techniques to improve the efficiency and reliability of predictive models, offering meaningful insights for researchers and policymakers in economic forecasting.

This study aims to provide a mechanism on long-term relationships between key economic variables, drawing on a vector error correction model with exogenous variables (SVECM). In doing so, quarterly data for the period 1374-1392 has been used to survey the long-run relationship between GDP, real liquidity, exchange rate, price index, a weighted average of foreign countries' production, oil export earnings and government deficit. The results show a positive impact in real liquidity's increase on output. The long-term effect of oil export earnings-due to its role in formation of capital-on production was positive, meaning that these earnings play an important role, even in long-term, in economic growth. Results confirmed the existence of fiscal dominance in monetary policy formulation, since 1% increase in non-oil government budget deficit could lead to an 0. 46% increase in real liquidity. The results show that an increase in real output and real liquidity, respectively, could decease and increase the real exchange rate. In long run, oil revenues has positive effect on real exchange rate, due to an increase in government controls on nominal exchange rate, that in turn can e caused by government budget's dependency on oil revenues, or cheap imports because of low exchange rate price.