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This study examined the correlation between economic growth and the impact on the environment, specifically focusing on the concept of environmental sustainability. The World Bank's Adjusted Net Savings (ANS) data is utilized in this study to gauge the strain on the environment, specifically through the measurement of natural disinvestment. This measurement encompasses the cumulative effects of carbon dioxide (CO2) damage, as well as depletions in minerals, energy, and forest resources. This study uses panel data with respect to the endogeneity of explanatory variables to estimate the real effect of per capita income and the other variables on environmental pressure. In this regard, employing the panel Fixed-Effects Instrumental Variable (IV) methodology, the data from 213 countries have been used in the period from 1990 to 2018. Through regression analysis, it has been discovered that there is a direct correlation between income and the impact on the environment in developing nations. However, this relationship is notably more pronounced in low-income countries compared to high-income countries. Additionally, the study reveals that trade expansion contributes to an increase in environmental pressure across all groups of countries. An increase in the school enrolment rate can affect the environment in developed and high-income developing countries. Moreover, the variable effect of capital openness on environmental pressure was estimated to be positive for developed and high-income countries. However, this effect was found to be negative for low-income countries. Finally, the result showed that developing countries should improve their legal structure and also reduce the bureaucracy and complexity of the laws.

In recent decades, the significance of the issue of climate change has escalated due to its intensified impacts, potentially diminishing or halting economic growth, particularly in developing countries and vulnerable sectors such as agriculture. Climate change may be considered the most important and complex human challenge. Among the economic effects, trade variables have been examined inadequately. Accordingly, the focus of this study is to investigate the impact of climate change on the export and import of agricultural products in Iran over a forty-year horizon, which was carried out using a dynamic input-output model. This study uses scenarios of temperature anomaly to examine the impact of climate change on different sectors of Iran’s economy. The findings indicate that climate change has a significant impact on the growth of both exports and imports of agricultural products. Under normal conditions without climate change, the average annual growth rate of agricultural product imports is 2.7 percent. However, this rate decreases to 1-1.8 percent when different climate change scenarios are taken into account. Regarding the exports, the corresponding value is 2.75 percent, expected to be reduced to 0.55-1.8 percent.  In addition, it was found that agricultural trade will be dominated by cereals import. Also, the total trade of the Iranian economy will change in favor of non-agricultural commodities

The examination of exchange rate management, interest rate, and capital market openness is one of the topics under discussion in macroeconomic policies. The assumption in maintaining equilibrium among these three policies is that any change in one of these policies requires an appropriate change in the combination of the other two policies, and there is always an equilibrium among them. The selection of three indicators; exchange rate stability, monetary policy independence, and financial market openness, is well known as the Mundell-Fleming model or the Impossible trinity. The current study explores the common determinants of exchange rate stability, monetary policy independence, and financial market openness using the panel data from a period of 2001 to 2020. Initially, using economic and structural principles, the common determinants of the impossible trinity policy are identified, then parameter estimation is carried out using the SUR model. Finally, it is examined how deviations from the impossible trinity assumption create a political pressure that may lead to a crisis unless policymakers adjust a proper combination compatible with it.

Portfolio allocation with Deep Reinforcement Learning (DRL) has been the focus of many researchers. In investing, a portfolio optimization strategy is selecting assets that maximize return on investment while minimizing the risk. Asset optimization involves balancing risk and return, where stock returns are profits over time, and risk is the standard deviation value of the asset's return. Many of the existing methods for portfolio optimization are essentially the expansion of diversification methods for assets in the investment. Signiant drawdowns and early entry into the share are still challenging in portfolio construction. The idea is that having a portfolio based on net money flow is less risky than allocating a portfolio based on historical data only and turbulence as risk aversion. This paper proposes a profitable stock recommendation framework for portfolio construction using the DRL model based on the net money flow (MNF) indicator. We develop a new risk indicator based on the intelligent net-flow behavior of smart money to help determine the optimal market timing for buying and selling. The experimental results of real-world trading scenario validation show that the model outperforms all the considered baselines and even the conventional Buy-and-Hold strategy. Moreover, in this paper, the effect of defining different environments made of various information with hyper parameter optimization on the performance of models has been investigated, and the performance of DRL-driven models in different markets and asset positions has been investigated. The empirical results show the dominance of DRL models based on MNF indicators.

Improving the team's psychological safety in multicultural situations requires attention to the cultural competence of leaders. This is despite the fact that prosperous industries and businesses related to the clothing field that experience multicultural and diverse environments in terms of diversity of human resources in Iraq have paid less attention to this issue. Based on this, the aim of this study is to recognize the cultural competence of leaders for psychological safety.The research method is exploratory and qualitative content analysis. The research community was experts and owners of clothing industries at the high and middle levels of companies and factories of clothing production, from which the purposeful sampling method dependent on the criterion with 22 people reached the data saturation. The data collection tool was a semi-structured interview. After implementing open and axial coding, the data were analyzed through MAXQDA software. Validation of data was done through external auditors and member checking. Finding showed that the cultural competencies of leaders for the development of psychological safety in work teams include team safety competencies, safe leadership competencies, and safety cultural competencies.