Nowadays there is a need for natural gas in almost every country. Considering the fact that Islamic Republic of Iran is the second owner of natural gas reserves in the world, export of this important factor would be one of the most inevitable subjects in our economy. Furthermore, the benefits of oil export can be replaced by the benefits of gas export. But the high level of domestic consumption is of the most restrictive factors in promoting gas export.The main purpose of this paper is to determine an optimum price for Iran's natural gas export taking into account the domestic versus international demand for the fuel. In this way we practice a mathematical approach with a linear planning model and considering the concept of opportunity cost in our analysis. In this way the optimum price would be reviled by adding the shadow price to the marginal cost. If we allocate our natural reserves to export, we should add the minimum benefits of domestic consumption to marginal cost of production of gas. Obviously it will result in minimum price of gas export which is estimated to be 48.8 cent per cubic meter.

The aim of this study is to introduce appropriate models for forecasting the gas oil prices in Singapore market which influence the gas-oil price in the Middle East. The used data are on weekly basis and covering the period of (1997-2010).The forecasts were made for 10, 20 and 30 percent ages of the data, separately. The models have been employed for prosecutions included four models of Neural Network and ARIMA model. The 4 selected Neural Networks are Feed-Forward Back Propagation, Cascade Back Propagation, Elman Back Propagation and Generalized Regression. The training functions are also Levenberg-Marquardt and Quasi-Newton BFG. The results that the least forecasting error belongs to the network in the Levenberg-Marquardt training function has been used. The results also reveal that for forecasts of 20 and 30 percent of data, Elman Back Propagation and for the 10 percent of the data Feed-Forward Back Propagation networks have the least forecast error. Moreover, the findings also reveal that Generalized Regression network and ARIMA have the largest forecasting error as compared to the other models. However, the findings of Diebold-Mariano statistics showed no statistically significant difference among the networks with the least forecast error, in respect to forecasting accuracy for the gas-oil price using a combination of 80 percent for training and 20 percent of day a for forecasting, as compared to the data combination, how less forecasting error. Finally, forecasting error of in the case of the best model is around 2 percent.

The main economic challenge of long-term contracts for the sale of natural gas through the pipeline is the pricing of the gas in the contract. Considering the impact of market developments on natural gas pricing in long-term contracts, the economic pathology of natural gas sales contracts and providing suggestions to reduce this damage become important. In long-term contracts, the price of gas is determined based on the price of alternative energies. This type of pricing means that the price of gas is not determined in real terms and subsequently it will require a lot of negotiations and transaction costs. This fact, which is not in any way favorable to the economic character of the parties, leads to the fact that the pricing is repeatedly subjected to price revision arbitrations. In order to avoid the above economic risks and create transparency in the gas market pricing system, it is better to conclude natural gas sales contracts in the form of spot and short-term contracts. In addition, the restructuring of the energy exchange and the launch of the gas market can help to establish transparency in the market.

The liberalization of natural gas markets and the emergence of gas hubs in recent decades have shifted the natural gas trade from the regional to the global trade. The growth and maturity of these hubs have weakened the previously established relationship between the natural gas price and the prices of crude oil and petroleum products. Therefore, predicting the price of gas as a strategic commodity has become more important for different countries. Using the neural network method, this paper attempts to develop a model of the monthly prediction of natural gas price. Based on the time series data from 2012 to April 2019 as the input to the neural network, this model predicts the prices in five hubs and natural gas exchange centers in Europe. Based on the R2 performance evaluation index of 98% of the neural network model fitted based on the aforementioned data series, the neural network model has acceptable performance in predicting the natural gas price. The results of this study show that using the artificial neural network (ANN) method, the gas prices in the European gas hubs, which are located in European countries, can be predicted with a high degree of accuracy.

Unlike many arbitrations, the arbitration of price reviews in natural gas industry is not based on allegations that one party has breached a contract or otherwise committed a legal wrong. Instead, arbitrators are asked to determine whether the economics of a contract should be adjusted and, if so, to establish a new price formula. Due to the complexity of revision of gas prices, the experts have significant role in these arbitrations. Expert advice will often be used extensively, particularly in establishing whether review has been triggered, and the existence of a significant/material change in the market. Regarding expert’s role, gas price arbitrations have specific nature. In this article, the specific nature of these arbitrations due to arbitrators` role in reviewing the terms of price adjustment in order to maintain the long-term contractual relations of the parties is examined by descriptive-analytical method and the interaction of these arbitrations with expert determination is reviewed. It`s established that despite the prominent role of experts, they are considered arbitration not expert determination as one of Alternative Dispute Resolutions (ADR).

Energy, as one of the important inputs of production, has a special place in the economic development and progress of every country. Therefore, checking the price of gas can be important for Iran and countries with oil and gas resources. In addition, based on the past trend of gas prices in the world market, we can conclude that gas prices have always been affected by global oil prices. Considering the role of specialized labor in the two oil and gas sectors of the studied countries, it is important to be able to determine the simultaneous effect of the gas price and the total factor productivity on the transfer of labor between these two economic sectors. This research used the dynamic computable general equilibrium model and the social accounting matrix of 2014, and according to the dynamics of the model, the change process has been predicted for the years 2022-2030. The results indicate that, with the increase in the price of gas and the total factor productivity, skilled and unskilled labor is transferred from the oil sector to gas for Iran and the GECF, and this trend is also in the long term. Also, this impulse led to an increase of capital and land used in the gas sector of gas exporting countries by 88 and 57% respectively, and in Iran, capital and land used in the gas sector were estimated 5 and 5%. Therefore, bargaining to increase the price of gas and using additional methods to increase productivity can lead to structural changes that to serve the economic growth of Iran and GECF.

International gas sale contracts, as one of the key instruments in energy trade, face significant challenges—particularly in the area of price (consideration) determination. Characteristics such as long-term duration, dependency on global markets, and sharp fluctuations in energy prices have led to the adoption of dynamic and flexible pricing mechanisms in these contracts. However, the absence of clear and harmonized legal frameworks—especially within the Iranian legal system—has resulted in contractual conflicts and a reduction in economic efficiency. This study aims to conduct a comparative analysis of pricing mechanisms in international gas sale contracts, drawing on international legal and economic sources, arbitral practices, and real contract samples, in order to propose legal improvements for Iran in this field. The findings indicate that price determination methods may include the use of energy market indices, adjustable pricing formulas, futures contracts, or referral to independent experts. Additionally, mechanisms such as periodic price adjustments, renegotiation clauses, and price review formulas are considered effective tools for mitigating economic risks. Nonetheless, Iran’s legal system faces limitations in fully implementing these mechanisms due to jurisprudential constraints and the lack of explicit legal provisions. This study underscores the need for legal reforms in the field of energy contracts, including the formal acceptance of flexible pricing mechanisms, development of regional pricing benchmarks, and incorporation of protective clauses in long-term agreements. Implementing these reforms could pave the way for enhancing Iran’s position in the international gas market.