اقتصاد مقداری

The main reason for people investing in the stock market is to make a profit, which requires accurate information about the stock market, price changes and predicting its future trend. Therefore, investors need powerful and reliable tools for forecasting Stock prices in the future. The main purpose of this study is to present a method based on wavelet denoising and dynamic time warping to identify the stock price pattern in the Tehran Stock Exchange. In this regard, first, using the wavelet denoising preprocessing step, noise is removed from the stock price time series, and then the extracted data is used as input to the dynamic time warping prediction model. MATLAB software version 9.11 was used to analyze the research data. The statistical population of the present study includes 3 shares among the shares of steel industry companies of Tehran Stock Exchange. The research was conducted in the period 1395 to 1398. The results show that the predictions obtained from the dynamic time warping method equipped with the wavelet denoising preprocessing step in comparison with the predictions obtained from the dynamic time warping method without the wavelet denoising preprocessing step in all three shares studied, have been associated with much less accuracy and error.

EXTENDED ABSTRACT INTRODUCTION The purpose of this study is to analyze the concept of "developed man" and determine the degree to which attention is paid in the Fundamental Transformation Document (FTD) in education in Iran approved in December 2011. This concept has been developed based on the theoretical basics of classical economics on description of "economic man" and also the Engels’s theory on the description of "modern man" and with regard to some of adapted and localized national and Islamic ideas and beliefs. What is meant by "Developed man" in this research is a person whose attitude and opinion has been developed and has the ability to turn the country's resources into wealth and prosperity for the people of society.  METHODOLOGY The instrument by which one can measure it, is a researcher-made content analysis checklist that involves 25 characteristics for developed human, and its face reliability and validity have been also verified and ranked by subject matter experts. Shannon entropy indices have been used to determine the importance and ranking of developed human components. In order to achieve a valid explanation and transparent results in this study, some important and fundamental concepts and components that were analyzed in the literature reviewing this document and their frequency was determined, were selected, and their frequency was compared with the total frequency of developed human components in the transformation document text. FINDINGS By use of word-counting the whole text of the Fundamental Transformation Document of education system, it also became evident that only about 1% (113 words out of 10914 words available) of this 64-page document are dedicated to the components of developed human and as a result it can be said that with regard to the weight and importance of the economy and its related issues in the country (such as unemployment, weak attitudes and skills of wealth creation and lack of efforts to increase production and improve public living and poverty alleviation) from the leaders and policymakers viewpoint, especially. CONCLUSION In recent decades, little emphasis and attention has been paid to developed human, and accordingly, it is worthwhile to pay close attention to these components in revision of the Transformation Document text.

EXTENDED ABSTRACT INTRODUCTION In the oil market, the price of oil depends on the international supply and demand of oil. In general, the factors affecting the price of oil can be divided into two main groups: fundamental factors and non-fundamental factors. Oil has both physical and commodity aspects as well as assets. Changes in fundamental and non-fundamental factors lead to changes in oil prices. Supply and demand are among the fundamental factors and risks and uncertainties, psychological, political, and geopolitical factors, stock exchange and speculation activities in oil financial markets, etc., which cause sudden changes in oil prices are among the non-fundamental factors. One of the signs of the existence of uncertainty in an economic system is high fluctuation in economic variables. The interaction between oil performance and macroeconomics has long attracted attention in the economic literature. Attention to this issue dates back to the 1970s. The 1970s and early 1980s were marked by sharp jumps in oil prices. Adverse oil supply shocks are often considered the main source of macroeconomic fluctuations and stagflation in that period (Blinder & Rudd, 2008).The present study differs from other studies in two ways compared to other studies. In internal studies, uncertainty related to variables is mainly measured through the conditional heterogeneous variance pattern. The data is an indicator of uncertainty in this study based on the method of Baker et al. (2016) and based on the media. When analyzing economic phenomena in a regression framework, economic theory seldom provides distinct information about the functional form of the relationship between the dependent variable and its independent variables. The simple properties of regression functions – such as models that are linear in parameters – are widely used in experimental applications, as they facilitate both econometric estimation and the economic interpretation of estimated regression parameters. However, the assumption about the functional form of the regression function leads to an error of incorrect parameter specification, which can lead to incorrect economic results and recommendations. In recent years, the rapidly growing literature on nonparametric econometric methods has been a solution to the problems of parametric specification error in econometric regression models. Nonparametric regression methods do not require the researcher to assume and determine a functional form for the relationship between explanatory variables and dependent variables. Thus, the functional form is determined by the data instead of the researcher making arbitrary decisions. In this aspect, the present study is different from other studies. Therefore, in the present study, considering the role and importance of OPEC in the oil supply and demand and oil prices and its effect on the growth and production of wealth in the world, factors affecting the determination of OPEC oil prices with emphasis on economic policy uncertainty as a representative of non-fundamental factors affecting oil prices, are investigated for a selection of OPEC member countries from 2003-2017 using non-parametric regression. METHODOLOGY The methodology must be clearly stated and described in sufficient detail or with sufficient references. The author should concisely summarize the research framework, and the research methods applied in the study. The summary should include the identification of valid data sources used in the research.The statistical population in this study is OPEC member countries and the data consist of 6 cross sections between the years 2003-2017. The data were extracted from World Bank reports, the OPEC Database[1], the Economic Policy Uncertainty Database[2], the World Bank Database[3], and the OECD website[4].The variables of the OPEC real oil price model are dependent and independent including uncertainty of economic policy, industry value added index, real interest rate index, oil rents, renewable energy, and oil price expectations.                                                                  FINDINGS  By using the kernel regression: local linear estimator, the extent and direction of the effect of global economic policy uncertainty on oil prices is estimated. The table of meanings of the studied variables is given in Appendix One. The results show that the alternative energy index and the interest rate index are fixed with a durable interruption. Also, according to the diagram shown in Appendix 2, in most of the countries studied, the value-added index of the industrial sector, affects the real price of oil by interruption.In this section, using the findings of kernel regression: local linear estimator in Stata software, the research hypotheses are examined as follows:Uncertainty in global economic policy has a positive and significant effect on oil prices.Among the influential factors in oil prices, economic policy uncertainty has the greatest effect. Table 1. Core regression: Local linear estimator for 6 OPEC countriesSource: Resarch ResultLocal-linear regressionKernel: epanechnikovBandwidth: cross-validationNumber of obs      =           327 R-squared           =          0.9678Log opEstimateMeanLog op 4.210966EffectLog un Ope L rr Or L ind  L re 0.3522596 0.420129 -0.0003032 0.0209778 0.0169741 -1.228478 Based on the table, the results show that there is a positive and significant relationship between oil prices and economic policy uncertainty. In terms of the effect on oil prices, the Alternative Energy at -1.2, the Oil Price Expectations at 0.42, and then the Economic Policy Uncertainty at 0.35 have the most effect, respectively.According to the findings, it can be concluded that for a one percent change in the economic policy uncertainty index, the variable of oil price will change by 0.35 percent. For one unit change in the oil price expectations index, oil prices increase by 42%. The real interest rate with a period of 0.03 percent reduces the real price of OPEC oil; For one unit change in the oil rent index, OPEC oil price increases by 2 percent, and the value added of the industrial sector with a lag increases to the rate of 1.6 percent of the price of oil; the index of alternative and new energies with a lag leads to a decrease in oil prices by 12 percent. CONCLUSION The present study aims to investigate the effect of economic policy uncertainty on oil prices in selected OPEC countries. In determining the price of oil, fundamental factors including factors related to oil supply and demand, and non-fundamental factors including financial market conditions, speculation, and geopolitics are effective. Given that the countries under study are selected from OPEC and the production and sales quotas are determined by OPEC, factors such as the value added of the industrial sector in each country, alternative energy sources, oil rents, and oil price expectations are among the fundamental factors and the US Federal Reserve real interest rate and economic policy uncertainty are among the non-fundamental factors. Estimation of the model by nonparametric method shows that renewable energy has the greatest effect on oil prices so as the quota of renewable energy in the energy supply increases, OPEC oil prices will fall due to declining demand. The added value of the industrial sector has a positive and significant effect on oil prices. Because of the increase in industrial activities, the oil demand has increased and this will lead to an increase in oil prices. Oil rents have a positive and significant effect on oil prices as the difference between income from oil sales and oil extraction costs. Considering that the income from oil sales is due to the determination of oil production and sales quotas as well as oil prices in OPEC member countries, by reducing oil extraction costs the profitability of the oil industry has increased, and through increasing oil speculation, its prices increase. Oil price expectations, like market expectations for any commodity, affect prices. Expectations of rising oil prices will lead to higher oil prices due to increased precautionary oil demand. According to the research findings, an increase in the Federal Reserve's real interest rate will lead to a decrease in oil prices. On the one hand, raising interest rates increases the cost of maintaining oil on board, and on the other hand, it reduces the net present value of future profits, thus increasing oil supply. Rising interest rates on the Federal Reserve will reduce the price of savings bonds, treasury bills, and stocks. Thus, the profitability of speculative activities in the savings bond market leads to the transfer of cash flow from commodity exchanges such as oil and its derivatives and agricultural products to the savings bond and treasury markets. As a result, oil prices fall. The emergence and spread of economic policy uncertainty will lead to higher oil prices. Since oil has assets in addition to physical and commodity aspects, with the increase of economic policy uncertainty, the possibility of restricting the future supply of oil, as well as increasing precautionary demand and speculation of oil, increases the price of oil. Therefore, the unique situation of OPEC member countries means that the uncertainty of global economic policies is not the most important factor affecting oil prices, and the development of alternative energies has a much greater effect on oil prices, and this necessitates a change in policy and precise planning in oil-rich countries to use alternative energies and produce oil products instead of selling crude. Although the general policies of the system in the field of energy, the diversity of energy resources, and the replacement of exports of oil and gas and petrochemical products with exporting crude oil and natural gas have been emphasized, despite the development of a national energy strategy document in the country, operational and monitoring of its realization should be prepared in a suitable time horizon. [1] https://www.opec.org/opec_web/en/[2] https://www.policyuncertainty.com/[3] https://data.worldbank.org/[4] https://www.oecd.org/

EXTENDED ABSTRACT INTRODUCTION In the last decade, concern about the environment has increased increasingly around the world. One of the serious environmental challenges is urban solid waste, the management of which has been raised as one of the main concerns of human societies. According to the World Bank, by 2025, more than 1.4 billion people will live in cities around the world, and each of them will generate an average of 1.42 kilograms of municipal waste per day. Estimates show that municipal waste worldwide triples every year. The annual generation of municipal waste worldwide has increased from 0.68 billion tons to 2.2 billion tons. Based on available data from countries up to 2012, the World Bank has published a report focusing on municipal solid waste generation. Waste is materials or objects that are discarded or thrown away. Solid waste is waste or discarded materials and objects obtained from industrial, commercial, mining, agricultural, and general daily activities (Ugwu, Ozoegwu & Ozor, 2020). Solid waste is one of the serious environmental issues in developed and developing countries. Solid waste management is a major challenge in urban areas around the world, especially in developing countries. The main reason for this challenge is the rapid population growth along with the expansion of cities, the reduction of financial resources, and the weakness of urban planning. Human activities and changes in lifestyle and consumption patterns have led to an increase in waste production rates (Bovard & ilanloo, 2019). Controlling environmental pollution, including waste, is an important part of human duty in maintaining human health, which has a special place in new sciences and techniques according to economic health standards. Waste production is inevitable in human daily life and population increase will increase it. Municipal solid waste is defined as waste generated by human, commercial, and construction activities that are collected and treated by municipalities (Xiao, Dong, Geng, Tian, Liu, & Li, 2020). The main composition of these wastes is almost the same in different countries of the world. However, the amount of production waste, density, and share of each part of it is different from country to country and city to city. This difference is caused by economic development, geographical location, weather conditions, and cultural and social considerations (Afshar Kazemi, Eftekhar & Omrani, 2014). About, 2.01 billion tons of municipal solid waste is produced annually in the world, of which at least 33% is not environmentally managed. Worldwide, waste generated per person per day averages 0.74 kg but varies widely from 0.11 to 4.54 kg. Of course, it is predicted that by 2050, the amount of waste produced in the world will increase to 3.40 billion tons, which is equivalent to 2 times the population growth in that year. The East Asia and Pacific region produces the most waste in the world at 23%, while this number for the Middle East countries is about 6%. Of course, it is expected that the total waste production in this region will more than double by 2050. It is worth noting that in these areas, more than half of the waste is discarded without reuse. This waste growth will bring many adverse environmental, health, and welfare consequences. Therefore, it requires basic measures.     METHODOLOGY The purpose of this study is to evaluate the efficiency of circular economies in Persian Gulf countries in terms of Municipal Solid Waste Management. This research is applied in terms of purpose and descriptive survey in terms of data collection. Its statistical population was formed from Persian Gulf countries. In this research, the per capita value of MSW production and the dimensions of the social progress index "basic human needs", "basics of well-being" and "opportunity" are used as input and recycling as output in the model. SPI indicators show how well society can cover the needs of its members and improve their quality of life. Data on MSW generation and recycling rates were obtained from the World Bank and data on the three SPI indicators were extracted from the Essential Social Progress website. The data of this research is for the year 2021. The DEA model was implemented in LINGO Softer.   FINDINGS The findings show that the countries of Kuwait (1), United Arab Emirates (0.952), Saudi Arabia (0.654) and Iran (0.453) respectively have the highest circular economy performance compared to other countries in the Persian Gulf. The aim of this study is to analyze the efficiency of DMUs from the perspective of variable returns to scale (VRS).   CONCLUSION The main purpose of this evaluation is the efficiency of circular economy in the Persian Gulf region in terms of urban solid waste management. The results show that Kuwait and the United Arab Emirates respectively have the highest circular economy performance compared to other areas of the Persian Gulf. Data analysis shows that according to the statistics of the World Bank, these three countries recycle 21% and 20% of manufactured products respectively. They have good performance compared to other input indicators. It should be noted that these results can be partially under the conditions of the Covid-19 pandemic. As, Iran will produce more solid food with more population and monitoring of health protocols. This could have caused it to fall to fourth place. Data analysis shows that production per capita in Iran is much lower than other countries. Iran has an average performance in terms of SPI indicators. But in terms of long-term performance, it has been ranked fourth. The reason for this is its very low recycling. Policymakers should use advertising, education, etc. to increase the collection rate of recyclable products. In order to improve their performance, the system needs to produce and at the same time increase the recycling rate. Education and public investment can help in this regard. Also, it should improve its performance in terms of SPI indicators. The province also improves access to basic medical care, food, water and housing. Also, they should provide access to basic education and even advanced education for those in the country who wish to increase their knowledge and skills. According to the results, it is suggested that other Persian Gulf areas improve their performance in recycling. The country's data shows that they recycle less than 10% of their production. It is suggested to evaluate the performance of the Persian Gulf with Europe for a better explanation. It is also suggested to use other indicators of reuse in calculating circular economy performance. Since it is new for environmental management and circular economy at the study level. This means that the special indicators of recycling evaluation and SPI indicators in previous years are different from previous years and practically will not reach a comparative output. For this purpose, it is suggested that future researchers study in different years with an interval of 5 years in the same field and compare the results of different years. However, there is no validity and reliability of this research. But more thought is needed in generalizing the results.

EXTENDED ABSTRACTINTRODUCTION With the increase in population growth and the problems of the planet, researchers have paid more attention to the phenomenon and behavior of greenwashing. Greenwashing behavior is a situation according to which companies and businesses present unhealthy products and services as green and organic (Wang et al., 2019). This study generates new insights for emerging markets and provides potentially important information for academics and practitioners on greenwashing behavior. It can be shown that almost half of people's income is spent on buying food products (Mishra & Sharma, 2012), so the quality, safety and performance of such products have attracted the attention of customers, consumers and shareholders (Bhattacharya, al. , 2012).), in addition, environmental protection has faced problems in the country due to the limitations of greenwashing regulations and their unclear and imprecise implementation. In the field of food products, national regulations on organic and green labels have not been determined, so many companies and food industries can use greenwashing behavior well and show themselves to be environmentally friendly (Chen & Chang, 2012). With these explanations, it is possible that customers are skeptical about green claims and the purchase of green products in society will decrease, which in turn will have harm to people in terms of safety and health, and its consequences for the environment include It will be pollution and destruction of ecosystems (Hai et al., 2017). Due to having industrial companies, Khuzestan province is exposed to industrial and environmental pollution (Guo et al., 2014). In the past years, suggestions for minimizing the environmental effects of waste, access to green life, and maximizing the capacity of using green food industry products, which can reduce their potential effects by using the phenomenon of greenwashing, have not been presented by researchers. As a result, it is vital for consumers to review their product consumption practices and move from non-organic products to green products. By observing these cases, in this research, we will seek to investigate the phenomenon of greenwashing and explain the mechanism of its effect on the intention to buy green. METHODOLOGY The current research is in the field of descriptive and survey research from the methodological point of view. Also, from the point of view of the implementation method, the current study has two phases: library and field. In the library phase, the researchers presented the research model by using the literature review and the existing experimental background. The authors' tool for this has been the use of library resources. In the field phase, the authors distributed questionnaires in order to test the fit of the model and research hypotheses. Finally, 276 healthy questionnaires were collected and analyzed using the structural equation modeling method based on the partial least squares approach. FINDINGS The authors present the findings of this study in two basic steps. The first step of the findings is related to the fit and validity of the research model. The second group of findings of this research includes the confirmation of all research hypotheses, which are summarized in Tabel 1: Tabel 1. The results of the test of research hypotheseshypothesesPathPath coefficientT StatisticResultH1greenwash  Green Purchase Intention-0/3166/045supportedH2greenwash  Green Word of Mouth-0/81940/944supportedH3greenwash  Green confusion0/82740/476supportedH4greenwash  Green Skepticism0/77129/208supportedH5Green confusion  Green Purchase Intention-0/2695/833supportedH6Green Skepticism  Green Purchase Intention-0/1353/053supportedH7Green Word of Mouth  Green Purchase Intention0/2545/218supportedH8greenwash  Green confusion  Green Purchase Intention-0/1042/985supportedH9greenwash  Green Word of Mouth  Green Purchase Intention-0/2085/596supported  CONCLUSION The current research encourages and supports food industry marketers to create credible green claims that not only improve their business status, but also enhance their environmental programs. The results showed that some food industries have a high risk due to consumers' pessimism towards the greenness of their activities and services. Managers should focus on several environmentally friendly methods to reduce consumer cynicism. Managers should increase the transparency of environmental performance, and in addition, non-governmental organizations can audit companies' environmental reports and fine companies that engage in greenwashing. With these words, it is expected to be able to help control greenwashing behavior and green doubts to some extent.

EXTENDED ABSTRACT INTRODUCTION Scarcity and justice have always been the concerns of human beings. To make a choice between these two concepts, capitalist systems have given priority to scarce resources and the optimization of their use in the hope that economic growth will also provide justice. In recent decades, environmental degradation, increasing pollution, extinction of various animal and plant species, climate change and global warming have cast doubt on the unbridled growth approach adopted by the capitalist system. This approach has proposed the idea of ‘sustainable development’ as a solution to those problems. The concept of sustainable development, which was proposed in 1980, was actually a response to the destructive environmental and social effects and to the concept of economic growth in general, in such a way that this opinion originated from environmental trends (Zakerian et al., 2014). Based on this idea, it is possible to take advantage of development opportunities through the interaction and adaptation of social, environmental and economic goals. The approach focuses on sustainable development, which gives importance to the equitable distribution of economic growth benefits (social and environmental goals) and pays attention to scarcity (economic goals). On this basis Sustainable development is development that can continue for a long period of time without causing damage to the environment (Shafiei, 2018). These points bring one to the conclusion that, to exploit resources, not only economic interests but also social, environmental and economic goals should be taken into consideration.Since in recent years, the continuation of economic growth along with the development of urbanization and population growth has led to further destruction of the environment and the reduction of scarce natural resources, including water resources, this issue has led researchers to pay more attention to the environmental aspect of sustainable development. From the perspective of sustainable development, priority is given to activities that, while providing the most economic benefits, put the least pressure on water resources and the environment of the region (Nasrollahi & Zarei, 2017). In Iran as a developing country, environmental issues took on a wider and more visible dimension. Although Iran has devised measures to overcome environmental challenges, the gap with global indicators is large, and according to many activists in this field, Iran is approaching a multi-faceted environmental crisis (Rezayan ghayehbashi and Marzban, 2020). The location of Iran in the arid and semi-arid climatic region and the uneven distribution of rainfall have led to the aggravation of the water shortage in Iran, so that the increase in demand has faced the country with the limited supply of water resources.Therefore to prioritize the economic activities, it is necessary to pay attention to the water shortage issue and the other environmental considerations. In this regard, the present study has identified and prioritized the economic activities of Isfahan Province by using the criteria for different dimensions of sustainable development and with an emphasis on the importance of water resources. To this end, input-output models and the TOPSIS method are innovatively combined to provide a proper picture of prioritizing the economic activities in the province. The rest of this article is organized in several sections. The second section provides the research background. The third section presents the Background studies, and the forth section research method and the data. The findings of the study are put forth in the five section. The last section is dedicated to the conclusion of the study and suggestions for future research. METHODOLOGY Therefore, the present study prioritizes the economic activities in the province of Isfahan by combining an input-output model and the TOPSIS method as well as considering the six criteria of water abstraction, energy consumption, pollution, intensity of inter-sectoral relations, job creation, and value added. To achieve the objectives of the research, first, the national input-output table of 2016, the Central Bank data and the SFLQ quota method were used to prepare a twenty-sector input-output table for Isfahan Province. Then, using the internal multipliers of the province, the forward and backward linkages, direct and indirect water intensity, direct and indirect energy intensity, and direct and indirect employment of the economic sectors of the province were calculated. Shannon weighting method was also used to weight the criteria. Finally, the prioritization of the sectors in Isfahan Province was done with the TOPSIS method and the desired criteria. In the following sections, more details are provided on the methods collection in this research. Shannon Entropy MethodShannon entropy method served to determine the weight of each criterion in the study. This method is an alternative to using the opinions of experts, and it is advantageous in that the opinions and personal judgments of individuals are not involved in it; the weight of each criterion is determined based on a decision matrix (Dorostkar Ahmadi and Dehghani, 2020). There were a number of steps to determine the weights by Shannon entropy. First, all the decision matrices were normalized through Equation ‎(1) and the value of E (entropy) was estimated using Equation ‎(2)   In Equations‎(1) and ‎(2), , the values studied in the research and m and n represent the number of the sectors and indicators studied in the research.Next, the weight vector was calculated, and the weight of each criterion was determined using Equation‎(3).                Topsis ModelThe TOPSIS model, as a multi-criteria decision-making method, is a simple but efficient method of prioritization. It is one of the best methods in which m options are evaluated by n methods.This technique, which is a compensatory model based on MCDM methods, follows a mathematical logic. This logic first introduces a "positive ideal solution" and a "negative ideal solution". The positive one is a solution that increases the profit criterion and decreases the cost criterion. In other words, the negative ideal solution has the opposite value of the positive one. Then, all the examined options are compared with the best and the worst options, and the linear distance of each option from the best option and the worst option is measured. Finally, the option at the most distance from the worst option and the least distance from the best option is selected as the best or the optimal option (Khatami Firouzabadi et al., 2013).The TOPSIS method can be described as a sequence of steps as follows:1) Calculate the normalized decision matrix. This regards the formation of a decision matrix with a number of criteria and options. The criteria are placed in the columns and the options in the rows of the matrix.             2) Calculate the weighted normalized decision matrix. The weighted normalized value is calculated with Equation ‎(4) where  is the weight of j criteria, .=1 3) Determine the positive ideal and negative ideal responses: Thus, the best response (A+) is selected as the option that contains the highest values of the criteria, and the worst response (A-) contains the smallest values. In EquationError! Reference source not found.‎(6), O corresponds to the utility criteria, and I corresponds to the cost criteria.4) Calculate the Separator Dimensions: Using the Euclidean criterion, the distance of each point from the ideal response (positive and negative) is estimated through Equation‎(7): (The distance of option i from the positive ideal) (The distance of option i from the negative ideal)5) Calculate the relative proximity to the ideal response. The relative proximity of option to A+ is as Equation‎(8). There is also ∈ [0 to 1]. 6) Rank the priorities. Using Ri, the options can be ranked in a descending order. In other words, any option with a larger Ri has a higher priority (Moazzami Gudarzi et al., 2014). FINDINGS To show the importance of the simultaneous consideration of different criteria, the results of the study were prioritized once with the water intensity criterion and once without it. This was done with regard to the significance of water resources.For example, in the prioritization of the sectors according to the water intensity criterion, the agriculture sector was in the twentieth place, while this rank was the second in the prioritization without considering that criterion. Also, in the sector of "manufacture of leather apparel", this criterion was downgraded from the ninth to the thirteenth rank considering the water intensity criterion. Similarly, the sectors of "manufacture of textiles" and "manufacture of rubber and plastic products" were ranked 7th and 9th, respectively, while the ranks of these sectors without considering the water intensity criterion were fifteen and twelve. In the meantime, the priorities for the sectors of "wood and products of wood", "manufacture of machinery and equipment not classified elsewhere" and "construction" remained constant. CONCLUSION The results of this prioritization showed the importance of ranking the sectors in terms of water intensify as a criterion. As it occurred, through the TOPSIS method, the priority of most sectors emerged to be different from the results of prioritization with no water intensity considered. The difference was very significant in some sectors. For example, in the prioritization of sectors according to the water supply criterion, the agriculture sector was in the twentieth place, while it shifted to the second rank when prioritized without that criterion considered. Similarly, for the sector of manufacture of leather apparel, the involvement of the water intensity criterion downgraded the sector from the ninth to the thirteenth rank. Thus, the change in the rank of a sector depends on the water level of that sector. As it was found, the agriculture sector was in the second place in the prioritization without considering the water supply criterion, while the rank of this sector moved to twenty with the inclusion of the water supply criterion. This role of water intensity suggests the importance of paying attention to the water level. Therefore, it is necessary to prioritize the economic sectors in a region in accordance with the climatic conditions there. It is also advisable not to establish water-demanding activities such as steel industries and cultivation of highly water-consuming products in low-water areas.

EXTENDED ABSTRACT INTRODUCTION The number of revealed corruption-related crimes is one of the major challenges of Iran which has been significantly increased in recent years. According to official reports, although only two cases of embezzlement were reported in 1990s and 2000s with a total value up to $ 800 million, administrative corruption has significantly increased in 2010s with 13 large corruption cases and a total value up to $ 14 billion and a growth of over 1500% compared to 1990s and 2000s (The Iranian Students News Agency[1] (ISNA), 2017). The spread of such amount of corruption in administrative and bureaucratic system of the country can have irreparable economic and social consequences. The statistic investigations have shown that macroeconomic indicators including economic growth, employment, unemployment, poverty, inflation and income distribution have become less favorable in 2010 compared to previous decades (Statistical Center of Iran, Iran Statistical Yearbook, 2017). In this regard, the following questions can be raised: is there a relationship between corruption and the indicators of income distribution and poverty in the country? Since the volume of international and unilateral sanctions on Iran has increased in the 2010s, does such a widespread growth in the volume and value of corruption cases in Iran have a relationship with sanctions?   METHODOLOGY Therefore, the purpose of the present study is to investigate the effect of sanctions on causal relationship between corruption, income inequality and poverty in Iran during 1984 to 2020. For this purpose, the indices of per capita income, poverty line, administrative corruption and control of corruption, Atkinson and Gini have been utilized to investigate their interactions through Toda-Yamamoto Causality Test.   FINDINGS Findings of research show that:   sanctions have affected the causality of per capita income on administrative corruption. sanctions have affected the causality of corruption control on per capita income. sanctions have affected the causality of income distribution on per capita income. sanctions had no effect on causality of per capita income, on Atkinson Index and vice versa. sanctions had no effect on causality of per between poverty line, on administrative corruption and vice versa. sanctions had an effect on causality of per between poverty line, on corruption control and vice versa. sanctions had a significant positive effect on poverty line, but had no significant effect on GINI index. In other words, sanctions have affected the causality of income distribution on poverty line. sanctions had no effect on causality of poverty line, on Atkinson Index and vice versa. sanctions had no effect on causality of GINI index, on administrative corruption and vice versa. sanctions had no effect on causality of GINI index, on corruption control and vice versa. sanctions had a significant positive effect on administrative corruption, but had no significant effect on Atkinson index. In other words, sanctions have affected the causality of income distribution on administrative corruption. sanctions had a significant positive effect on Atkinson index, but had no significant effect on corruption control. In other words, sanctions have affected the causality of corruption control on income distribution.     CONCLUSION The purpose of the present study was to investigate the effect of sanctions on causal relationship between corruption, income inequality and poverty in Iran during1984 to 2020. For this purpose, the indices of per capita income p, poverty line, Atkinson, GINI, administrative corruption and corruption control were investigated. In general, the following results were obtained from the present study:   Income distribution is not an effective variable for poverty in Iran. Corruption is an effective variable for causality of poverty in Iran and its significance level is higher under sanctions condition. Corruption and poverty cannot properly explain the income distribution in Iran. However, the corruption control can be the cause of income distribution and poverty line is a proper representative for the cause of income distribution under sanctions conditions. Income distribution is a strong variable for causality of corruption in Iran. Poverty can properly explain the causality of corruption in Iran under sanctions condition, but is not the cause of corruption under normal condition.   According to the obtained results, it seems that sanctions condition is an effective variable for the relationship between variables of income distribution, corruption and poverty. However, the effective factors of income distribution need further investigations in future.