مطالب

In today's rapidly advancing electronics industry, the widespread use of electrical and electronic equipment has made sustainable management of end-of-life (EOL) equipment a pressing need. A global system is required to facilitate informed decision-making regarding e-waste. Manufacturers must therefore devise strategies for effectively managing products during the EOL phase. This article introduces a novel framework for managing e-waste during the EOL phase to identify the most appropriate EOL options to minimize e-waste. The proposed framework harnesses the power of Twitter as a data source, utilizing data mining techniques to extract consumer opinions on e-waste. The article also outlines the development of a Fuzzy Analytic Hierarchy Process method and a multi-objective mathematical framework, enabling efficient decision-making in EOL processes based on e-waste information obtained from Twitter data. The study's findings have the potential to assist equipment manufacturers in identifying flaws in electrical and electronic equipment by analyzing customer opinions shared on social media platforms. Furthermore, by analyzing consumer sentiment towards e-waste, manufacturers can gain a deeper understanding of customer perspectives and develop appropriate strategic plans for selecting suitable EOL processes for returned products. This study underscores the significance of social media data in addressing e-waste management challenges, showcasing the profound impact of customer opinions.

In this research, the effect of changes in energy prices on output of different sectors by using of regional input output of Hormozgan province is investigated. The AFLQ method is used for regionalization of national I_O table. Three scenarios for the increase in energy prices have been considered. The results showed that except of water, electricity and gas/ sector, the output of other sectors has decreased. In the first scenario, the increase in the output of Water, Electricity and Gas/ sector was equal to 38574.52 million Rials whereas the output of transportation sector had the largest decrease in production. Industry and agriculture sectors are the other sectors that have the largest decrease in output after transportation.Total decrease of regional output in first scenario was 1896633.17 million Rials. As a result of the research, 20% increase in the price of energy carriers, about 0.28% of the total production of the province has decreased. In the second and third scenario total decrement of regional output was 512973.80 and 3048927.86 million Rials, respectively. Decrement of agriculture output in these two scenarios was 362099.15 and 512973.80 million Rials. Considering the importance of the agricultural sector in food production, supporting the production of this sector is necessary using appropriate policies such as guaranteed purchase or decrease of tax rate and technology subsidies in different sectors. These policies can provide the necessary incentives to adjust towards more efficient energy technologies, in order to ultimately reduce the negative effects of the increase in the price of energy.

The Competitive Industrial Performance Index (CIP) measures a country's ability to produce, add value, export, and impact global trade through manufacturing industries. To improve industrial competitiveness, focus must be given to expanding production and enhancing its quality with technological advancements. Developing countries need to build technological capacity, increase production, and invest in infrastructure to upgrade their industrial competitiveness. However, Iran's Competitive Industrial Performance has fallen behind, lacking a favorable position in the region and the world. The annual reports of the UNIDO analyzing data from 1990 to 2020 shows that Iran's performance has been weak compared to similar economies. The gap between Iran and the global benchmark (Germany with a score of 0.416) and the regional benchmark (Turkey with a score of 0.117) has widened over the past three decades. Additionally, Iran's manufacturing industry production and export structures have experienced two different directions of transformation in the past two decades. From 2000 to 2010, concurrent with the Third and Fourth Development Plans, the Manufacturing Value Added share in total GDP (MVAsh) increased from 9% to 14%, and the Medium- and High-tech manufacturing Value-Added share (MHVASH) in total manufacturing value added increased from 41% to 45%. However, during the years 2010 to 2020, concurrent with the Fifth and Sixth Development Plans, both of these mentioned indicators regressed. Notably, the regression in the level of technology for high-tech products, from 0.9% to 0.5%, is continuously declining and poses a fundamental challenge for Iran's industrial competitiveness.

This study employs a quantitative approach, utilizing the gravity model and FMOLS estimation, to examine how economic sanctions affect the trade relationship between Iran and Nigeria. Additionally, it explores the influence of factors such as GDP, exchange rate, strong sanctions, and weak sanctions. By doing so, this research contributes to the existing knowledge on bilateral trade between these two nations and provides valuable insights into areas that require attention for fostering trade development between them. The research findings reveal that in the bilateral trade relationship between Iran and Nigeria, there exists a positive correlation between GDP and weak sanctions (LIM) with trade. An increase of 1% in GDP leads to a 7.79% increase in trade, while a 1% increase in weak sanctions contributes to a 3.91% increase in trade. Conversely, strong sanctions and exchange rate have a negative impact on trade, with a 1% increment in strong sanctions resulting in a 1.18% decrease in trade, and a 1% increment in exchange rate leading to a 1.96% decrease in trade.

During the last three decades, financial sanctions have been imposed on Iran by the United States, the European Union, and the United Nations Security Council. This paper aims at estimating the effect of financial sanctions on the import of capital and intermediate goods in Iran, which was carried out for two independent time periods. The first period (2010-2013) includes multilateral financial sanctions, and the second period (2016-2019) includes multilateral sanctions and the withdrawal of the United States from the JCPOA. We examined the impact using the difference-in-difference (DID) method. The results of the first period indicate that the decrease in the imports of capital and intermediate goods in Iran depends more on the countries that "provided the sanctions plan" than the countries that did not provide the sanctions plan, because the coefficient of dummy variable for implementation in the random effects model is statistically significant. The negative effect of 0.007 on imports shows that the effect is weak, because this group of countries behaved differently. In the second period, the random effects model is statistically significant. In this model, the negetive effect of 0.22 on imports indicates a significant effect. Therefore, the reduction of Iran's imports in this period depends more on the countries that provided the sanctions plan than the countries that did not provide the plan.