پژوهشنامه اقتصاد انرژی ایران
پیاپی 36 (پاییز 1399)

The high average life of onshore facilities, entering the second half of the life of large fields, reducing the recovery factor of oil reservoirs and Iran's backwardness from the development of common fields are the most important challenges of the upstream part of Iran's oil industry.Due to the impossibility of financing and necessary capital from domestic sources, it is necessary to pay more attention to foreign investment and its contractual methods in this field. Therefore, in this study, the financial-economic performance of Iran's service contracts model and Iraq is being studied and compared in terms of attracting foreign investment and financing projects for the development and exploitation of oil fields. in this regard, the financial simulation technique and sensitivity analysis of the contractor's rate of return on the changes in the financial parameters of the contractual models have been used. The results show that the IPC contract model provides better economic results for the contractor compared to buy back while motivating the contractor to achieve safe production, but the Iraqi service contract model due to the shorter payback period, which facilitates financing the project and reduces the risk of capital expenditure, especially at high oil prices is more attractive to the contractor.

Air pollution concerns, climate change, and sustainable development necessitate the discussion of the dynamics of energy intensity. Nowadays, energy convergence is widely used as a tool for considering the dynamics of energy intensity. The energy intensity convergence in manufacturing industries is suitable to assess whether there is a knowledge spillover between manufacturing industries and whether government policies have been effective for reducing energy intensity in manufacturing industries. The purpose of the study is to examine energy intensity convergence in Iranian manufacturing industries. To do that, we collected data from nine manufacturing industries from 1995 to 2015 and employed the generalized method of moments in panel data (GMM) technique. The results of the model estimation show that there is  convergence of energy intensity in the manufacturing industries.

In economic theories, Fiscal decentralization represents the shifting of decision-making power about the composition of tax revenues and non-tax revenues or expenditures are from the government to local units to provincial government officials, which leads to increased efficiency in resource allocation. And, since the pioneering study of Oates (1972), fiscal decentralization and its effects have garnered significant attention among economists. In the past years, global warming, carbon emissions, climate change due to increased energy consumption have brought the issues of fiscal federalism versus provincial institutions to the agenda of policymakers and energy research institutes. Because fiscal federalism can play a crucial role in reducing non-renewable energy consumption, promoting renewable energy, and reducing carbon emissions. Hence, energy economists have endeavored to shed on the association between fiscal decentralization and energy consumption and carbon emissions. In this way, this paper examines the effect of fiscal decentralization, in the form of shifting revenues and tax incomes responsibilities from the government to provincial institutions, as well as raising of the urbanization, GDP, and energy price on energy consumption for 31 Iran provinces from 2006 to 2020. To this end, this paper employs Quantile Regression with Non-additive Fixed Effects to analyze the impact of fiscal decentralization on energy consumption. The results of this study show that fiscal decentralization has a non-linear relationship with energy consumption. In addition, the rising urbanization and GDP lead to high demand for energy consumption. Moreover, the rising energy price leads to low energy consumption.

According to apposite and ambiguous opinions about the effects of foreign direct investment and innovation on environmental performance in developing countries, in this research despite the environmental Kuznets curve (EKC) hypothesis, pollution haven hypothesis (PHH), and rebound effects hypothesis (REH) in D8 countries (Iran, Turkey, Malaysia, Indonesia, Egypt, Pakistan, Bangladesh, and Nigeria) were studied. For this purpose, the required data was gathered from World Bank during the years 2000-2018. Also, the fully modified ordinary least squares (FMLOS) model and STATA software were applied for analyzing data. Results of variables description indicated that during the studied period, Iran has the lowest average amount of FDI to GDP, the lowest average amount of per capita GDP growth, the highest average of the proportion of fossil energies to total energy consumption, and the highest average of the proportion of per capita CO2 emission. In addition, the results of the specification of the econometrics model showed that there is a U inverse relationship between GDP and CO2 in D8 countries. Therefore, the EKC hypothesis is approved for studied countries. Also, foreign direct investment has a positive significant effect (in 10%) on CO2 emission. Hence, the PHH hypothesis is approved for studied countries. Finally, innovation has a negative significant effect (in 5%) on CO2 emission. Hence, the REH hypothesis didn’t approve for studied countries.

Today, energy supply is one of the most important axes of the strategy of different countries due to its vital role in the continuation of technological and economic development of countries. The Petroleum industry, as one of the most important industries producing energy, has become an important and special industry due to the complexities of its production and extraction. Therefore, classification and prioritization of changes have taken place, and presenting a model of how to deal with changes can play an important and fundamental role in managing changes in executive projects. Of course, no project can completely eliminate the risk of change, but it can be largely prevented by identifying the underlying causes of change.In this study, an attempt has been made to compile a study and present a questionnaire and interview with experts and specialists in the oil industry, information, and existing records, which resulted in finding 20 of the most effective changes in EPC projects in the field of the petroleum industry. For this purpose, after reviewing and analyzing the changes, the model of dealing with the changes in EPC projects is presented and then according to the main parts of EPC projects, the most effective cases of each part are determined by using multi-criteria decision-making methods consisting of series. Analysis Hierarchy Process (AHP) and Gray Relationship Analysis (GRA) have been proposed to provide a change management structure in EPC projects in the oil industry. Based on the obtained results, it was found that the design and engineering (E) phase has the greatest impact on the changes made in EPC projects, followed by the manufacturing and construction (C) and procurement (P) phases. This research could be useful for prioritizing and managing change and minimizing the impact of unintended changes on EPC projects in the petroleum industry

The characteristics of crude oil and the factors affecting the price of this energy carrier have made its price forecast always considered by researchers, oil market participants, governments, and policymakers. Because the price of crude oil is affected by many factors, ongoing studies should be done to make more accurate and reliable estimates over time. In this paper, a combination of GM (1,1) and ARIMA models and a hybrid model (GM-ARIMA) for crude oil price forecasting is proposed. The Brent crude oil price data for seasonal (2015Q1-2021Q2), monthly(2020m3-2020m12), and weekly(w12-2020: w16-2021) periods were used to examine this method. The results show that based on the evaluation criteria of mean absolute error percentage (MAPE) and square mean square error (RMSE), the evaluation criteria of MAPE and RMSE in the combined GM-ARIMA model are always lower than the GM and ARIMA models alone. Therefore, the GM-ARIMA hybrid model will be able to predict more accurately than the GM and ARIMA models. Therefore, for more accurate prediction, the GM-ARIMA hybrid model can be used instead of single models.