Prediction of CO2 Emissions in Iran using Grey and ARIMA Models

The examination of economic aspects of gas emissions and its consequences is very important, especially in terms of its volume at the current increasing trend. Therefore, the prediction of air pollution emissions of carbon dioxide can give the correct direction to policies adopted.  Hence, studying and forecasting of gas emissions is necessary. The purpose of this paper is the prediction of CO2 emissions based on Grey System and Autoregressive Integrated Moving Average and comparison of these two methods by RMSE, MAE and MAPE metrics. The results show the more accuracy of Grey system forecasting rather than other methods of prediction.  Also, based on the estimated results, the amount of carbon dioxide emissions will reach up to 925.68 million tons in 2020 which shows an increase of 66 percent growth compared to 2010 which is highly significant. Keywords: Carbon Dioxide Emissions; Forecasting; Grey system; Iran JEL Classifications: C22; C53; Q5

Investigating the Effects of Banking Resources Shock on Consumption and Investment in IRAN, by DSGE Approach

In the banking system, withdrawal deposit and decrease resources can cause shock in liquidity. On the other side, Bank-centered financial system in IRAN will affect on household consumption decisions  and production  and investment decisions of manufacturing companies. In this study, in order to survey the effectiveness of resources shock caused by Bank Runs on consumption and investment, New- Keynesian dynamic stochastic general equilibrium (DSGE) model with regard to the banking sector and relevant features is presented. Through this model the response of the variables mentioned against this shock is evaluated. The results of the simulation and estimation models with data that are made stationary in the period 1981 to 2018 with appropriateness of the proposed model for Iran's economy show that the theoretic model is compatible with the economic realities and banking resources shock will leads to consumption increase and investment decrease

Comparing Distortionary Effects of Iran Petroleum Contracts (IPC) and Production Sharing Contracts (PSC) Using Stochastic Dynamic Programming Model: The Case of South Azadegan field

This paper analyzes and compares the behavioral responses of the operator to the fiscal regime of the two types of contracts, Iran Petroleum Contract (IPC) and Production Sharing Contracts (PSC) with using the dynamic optimization approach (dynamic programming method). This paper aims to numerically compute the amount of distortions caused by the petroleum contracts, which creates some distortion in the investor's decision regarding to the neutral case that means there is no contractual restrictions including government share of resource rent, tax, extraction timing, cost recovery limit and so on. The focal point of this paper is the application of the stochastic dynamic programming for a real oil field in order to achieve the numerical results and using the deadweight loss (DWL) as an actual measure for assessment of the distortion of the contract regarding the first best case (neutral path). Accordingly, with using the information of the South Azadegan field, the results show that both fiscal terms of IPC and PSC have distortionary effects and the DWL of the IPC is more than that of PSC. For instance, in the reference scenario and reference oil prices the DWL of IPC and PSC are 22/22% and 21/14% respectively

PREDICTION OF CO2 EMISSIONS IN IRAN USING GREY AND ARIMA MODELS

The examination of economic aspects of gas emissions and its consequences is very important, especially in terms of its volume at the current increasing trend. Therefore, the prediction of air pollution emissions of carbon dioxide can give the correct direction to policies adopted. Hence, studying and forecasting of gas emissions is necessary. The purpose of this paper is the prediction of CO2 emissions based on Grey System and Autoregressive Integrated Moving Average and comparison of these two methods by RMSE, MAE and MAPE metrics. The results show the more accuracy of Grey system forecasting rather than other methods of prediction. Also, based on the estimated results, the amount of carbon dioxide emissions will reach up to 925.68 million tons in 2020 which shows an increase of 66 percent growth compared to 2010 which is highly significant