Journal of Economic Modeling Research

Studying the Impact of Trade Liberalization Policy on Energy Balance in Shanghai Cooperation Organization Member Countries (Regional Computable General Equilibrium Model Approach)

Abdolhamid Moarefi Mohammadi

Introduction
The growing energy imbalances in the country, which are caused by the higher growth rate of demand and consumption than supply and production, have made the need for planning to solve energy problems more evident than ever before. Undoubtedly, the continuation of the increasing trend of energy imbalances in the country's key carriers will cause economic, social and security effects and consequences. On the other hand, according to economic theories, trade liberalization increases efficiency, economies of scale, improves competition, improves the productivity of production factors and increases trade flows, and ultimately leads to economic growth. On the other hand, the Shanghai Cooperation Organization has great capacities in the energy sector (with about a quarter of the world's population, it controls 23% of oil, 55% of natural gas and 35% of the world's coal). Undoubtedly, the accession of observer countries, especially Iran, will increase the potential and capacities of this organization. Therefore, in this study, the impact of trade tariff reduction between Iran and the Shanghai Cooperation Organization on the balance of various energy types in Iran was scenario-based.

Method
No study has analyzed the impact of trade liberalization policy between the Shanghai Cooperation Organization member countries on the energy balance in Iran, and this study addresses this issue by using the computable general equilibrium (CGE) model. Also, among the computable general equilibrium models, the multi-regional general equilibrium model is specifically designed for analyzing world trade and can conduct research and studies on the international flow of goods and services and factors of production in a dynamic and static manner. Using a multi-regional general equilibrium model instead of a single-regional general equilibrium model has several advantages. One of the strengths of these models is their ability to help understand the relationship between sectors, countries, and factors of production on a global scale. Among the multi-regional general equilibrium models, the Energy-Based World Trade Analysis Project model provides diverse possibilities for world trade and energy-related research.
This study examines the impact of reducing trade tariffs between Iran and the Shanghai Cooperation Organization under scenarios of -25%, -50% and -100% on the balance of various energy sources in Iran, including crude oil and petroleum products, natural gas, coal and electricity. For this purpose, the necessary data were extracted from the Global Trade Analysis Project for Energy-Based (GTAP-E) version 10 database, which includes the Social Accounting Matrix (SAM) of 141 countries or regions and 65 sectors. Finally, the data were analyzed using MATLAB software.

Results and Discussion
The results showed that reducing trade tariffs between Iran and other member countries of the Shanghai Cooperation Organization, on the one hand, due to the ease of exporting energy carriers (especially crude oil and petroleum products) and on the other hand, due to the increased use of fossil energy exploration, production, and distribution technologies (especially crude oil and petroleum products, natural gas, and coal), leads to a decrease in fossil energy consumption and an increase in the net balance of fossil energy in Iran. In addition, the reduction of trade tariffs between Iran and other member states of the Shanghai Cooperation Organization, due to the possibility of increasing imports of goods and equipment that consume less energy needed in various domestic, industrial (light and heavy industries), transportation and agricultural sectors (tractors, combines, etc.), as well as increasing cooperation in the development of renewable energy technologies, will lead to an increase in the consumption of renewable energies and a decrease in the consumption of the energy carriers under consideration (especially electricity), and ultimately an increase in their net energy balance in Iran.

Examining Shocks of Factors Affecting Inflation: A Random Forest with Recursive Feature Elimination (RF-RFE) and Bayesian Vector Autoregression (BVAR) Approach

Mohammad Reza Lotfalipour

Inflation, as a key indicator of economic performance, directly affects household purchasing power, price stability, and the long-term planning of firms and governments. Uncontrolled inflation not only reduces public welfare and exacerbates social inequalities, but also disrupts investment and sustainable growth by creating instability in economic expectations. Therefore, identifying the drivers of inflation and understanding their transmission mechanisms is essential for designing effective monetary and fiscal policies. This study investigates the impact of economic shocks on the inflation rate by employing a combination of two approaches: first, the random forest-based variable selection method with recursive feature elimination (RF-RFE) to identify the most influential factors, and second, the Bayesian vector autoregression (BVAR) model to analyze the time dynamics and mutual interactions of these shocks.The dataset covers 42 economic variables from the first quarter of 2009 to the fourth quarter of 2021, grouped into seven categories: supply, demand, monetary and banking, taxation and budget, exchange rate, energy, and employment. In the first step, the RF-RFE method identified the most important determinants of consumer inflation. The results indicated that five key variables producer inflation, value added in the oil and gas sector, quasi-money, the market exchange rate, and banknotes and coins in circulation play a major role in explaining consumer price fluctuations.The subsequent BVAR analysis showed that shocks originating from producer inflation and the exchange rate exert strong short-term effects on consumer inflation. By contrast, variables such as oil and gas value added play a moderating role in the long term, gradually alleviating inflationary pressures. Furthermore, the variance decomposition of forecast errors suggests that, in the long run, exchange rate volatility and liquidity changes driven by quasi-money increasingly account for fluctuations in inflation

Estimating the Minimum Required Capital for the Financial Sustainability of Pension Funds: A Case Study

Abbas khandan

Objective: Despite their important role in Iran's welfare and economic system, pension funds have faced financial instability and serious threats in recent years due to financial challenges, especially the cash balance deficit. The aim of this study is to answer the hypothetical question of how much capital and assets is required at minimum to cover the deficit and liabilities of these pension funds.
Methodology: In this research which is a case study for one of the Iranian pension funds, by using two methods of futurology and the Value at Risk (VaR) models, an attempt has been made to estimate the minimum required capital for the financial sustainability of pilot pension fund.
Findings: The results show that in the scenario writing method, the minimum capital required to cover the deficit of this pilot pension fund in four scenarios based on the bond rate, ideal, optimistic and realistic, is on average more than 517 trillion tomans of assets for the year 1402. In the Value at Risk (VaR) method with different parametric (ARIMA-GARCH models) and non-parametric (Monte Carlo and bootstrap simulation) approaches, it was determined that this pilot pension fund needs on average more than 550 trillion tomans of assets for the year 1402 in order to cover its deficit with investment income. The results of this article considering the size of pension funds can be easily generalized to other funds and, thus, can be useful in adopting reform policies for financial sustainability in general.

Economic Modeling of the Impacts of Natural Gas Imbalance on the Value Added of Various Economic Sectors in Iran

Mohammad Sayadi

Natural gas is currently the most important energy carrier in Iran’s production and consumption mix, and its share is expected to increase in the coming years. Any imbalance in the supply and demand of this key energy source can have significant effects on the value added across various economic sectors. The main objective of this study is to analyze the impact of natural gas imbalances on sectoral value added and Iran’s gross domestic product (GDP), using an updated input–output model based on 2023 data. Four scenarios are considered: (1) no prioritization of sectors under imbalance conditions; (2) prioritization based on social and political considerations; (3) no prioritization along with the absence of consumption management policies; and (4) a combination of social-political prioritization with lack of consumption control policies. The results indicate that sectors such as “chemical products manufacturing,” “natural gas production and distribution,” and “electricity generation and distribution” suffer the most significant declines in value added, output, and employment due to their direct and indirect dependence on natural gas. In contrast, sectors like “motor vehicle manufacturing” and “poultry farming,” which are minimally dependent on gas, experience relatively lower economic losses. Moreover, under the second scenario (12% imbalance without affecting final demand), GDP in 2041 is projected to decline by about 3% more compared to the third scenario (21% imbalance with uniform impact across all sectors).

Credit Rating of Real Bank Customers Based on Logistic Regression and MPLE Logistic Methods and Neural Network

Seyed Ahmad Ameli

By designing an efficient loan management system, banks can increase efficiency and reduce the probability of non-repayment of principal and sub-loans. In this paper, the efficiency of logistic regression models, artificial neural network, was examined to predict the credit risk of real customers or in other words, applicants for microloans, which include a large group of customers in the country's banking system. Given the imbalance of the number of data, the optimal threshold was calculated using two sensitivity and detection curves, and the credit risk of each model was extracted from this method. In logistic regression, the compensated maximum likelihood method was used to estimate the coefficients considering the small number of bad customers instead of the maximum likelihood method. Finally, the accuracy and precision of each model was examined with multiple criteria. Using the Rock curve, the resolution of the models was examined, where the neural network model had the best resolution. Then, by comparing the MSE, RMSE and MAE errors, the efficiency of the methods was compared, and the performance of MPLE logistics and neural network is almost the same. Finally, considering the bank's goal in three scenarios of minimum credit risk, identifying good customers and separating customers, neural network, MPLE logistics, and in the third scenario, neural network and MPLE logistics simultaneously have been selected as the best models.

Artificial Intelligence Technology as an Anti-Poverty Policy: International Evidence and Lessons for Iran

Younes Nademi

Objective:Artificial Intelligence (AI), as an emerging and transformative technology, is still in its early stages of development, and many aspects—particularly its economic and social dimensions—remain underexplored. Given the critical importance of eliminating absolute poverty as the first of the United Nations Sustainable Development Goals (SDGs), the present study aims to investigate the effects of investment in artificial intelligence on poverty and identify the main channels through which this impact occurs in countries leading in AI technologies.
Materials and Methods: This study empirically employs panel data from 20 selected countries during the period 2017–2023 using the generalized method of moments (GMM). The main variables include investment in AI technologies as the explanatory variable, and both income-based and multidimensional poverty indicators as dependent variables. Additionally, the study analyzes the effects of control variables including economic growth, income inequality, health index, and human capital.
Results: Empirical results indicate that investment in AI technologies significantly reduces both income-based and multidimensional poverty. AI contributes to poverty alleviation by enhancing economic growth, improving agricultural productivity, enabling financial inclusion, facilitating access to educational and healthcare services, and increasing the precision of targeted subsidies. Furthermore, economic growth and improvements in health indices reduce poverty, whereas increased income inequality exacerbates poverty.
Conclusion:The study emphasizes the importance of investing in legal and technological infrastructure to effectively leverage the potential of artificial intelligence for poverty reduction. Accordingly, policymakers in developing countries, including Iran, could benefit from developing supportive policies and strengthening necessary infrastructure to harness AI capabilities for poverty alleviation and economic well-being.
Originality:This research is among the first comprehensive empirical studies to examine the impacts of investment in artificial intelligence on both income-based and multidimensional poverty, identifying key channels of impact within countries leading in AI technology. The findings provide valuable insights for formulating technology-driven anti-poverty policies.