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EXTENDED ABSTRACT INTRODUCTIONIn 2019, Iran is ranked sixth among world countries and fifth among Asian countries (including Russia) in terms of CO2 emissions. Therefore, studying the CO2 emission elasticity of the production sectors of this country is significant and important for energy and environmental policymakers. What factors influence changes in CO2 emission elasticities? Which are the stimulants and which are the inhibators? The answers to these questions are useful in reducing and controlling CO2 emissions. In the present study, CO2 emission elasticities of production sectors are calculated, and then, with the aim of identifying CO2 emission elasticity stimuli, the changes in CO2 emission elasticities are broken down into different components. The methodology of this research is based on Input-Output analysis and decomposition analysis. The novelty of this paper is to determine and calculate the components of changes in CO2 emission elasticities using SAD. Guo et al. (2018) have presented a method for calculating CO2 emission elasticities based on the Input-Output analysis.METHODOLOGY Aim of this paper is to investigate the factors affecting CO2 emission elasticities, CO2 emission demand elasticity and CO2 emission production elasticity. In the first step, the elasticities are calculated and in the second step, changes of elasticities are decomposed. In this study, we have used input-output tables published in 2001 and 2011 by the Statistics Center of Iran. Due to the differences in the sector classification of the input-output tables of 2001 and 2011, we match some production sectors and finally take into account the 65 unified sectors. In order to calculate the CO2 emission of each production sector, we first obtain the total consumption of each energy for each year from the Iranian energy balance sheet, and then we allocate each energy consumption to production sectors and single household sector, according to input-output tables and the share of production sectors and the share of the household sector.                                                                                                                                                                                                                                                                   FINDINGS Findings show that the "Electricity generation, transmission, and distribution" sector has the most elasticity. The "Ghosh inverse matrix" effect is a strong stimulus to the CO2 emission elasticity of the sectors. This result indicates that the change in the share of output i, which is sold to sector j as an intermediate input, is a strong stimulus to increase the elasticity of CO2 emissions. These changes can be due to increased economic activities and the inefficiency of production structure.   CONCLUSION "Electricity generation, transmission and distribution" sector should be considered by energy and environmental policy makers due to having the highest amount and changes in CO2 emission elasticity than other sectors. Increasing the share of renewable energy in the energy consumption basket of production sectors, increasing energy efficiency (reducing energy intensity) by replacing new and advanced equipment with old and worn equipment and improving production structure can help reduce the CO2 elasticity and CO2 emission in Iran's production sectors. Finally, due to the high of CO2 emission elasticities in the "Electricity generation, transmission and distribution" sector, future research can focus on this area and suggest solutions to increase production efficiency and energy efficiency. Also, future research can focus on the production structure of production sectors and provide solutions to improve the production structure of Iran's production sectors.

The modelling of strategies for buying and selling in Stock Market Investment have been the object of numerous advances and uses in economic studies, both theoretically and empirically. One of the popular models in economic studies is applying the Semi-parametric Markov Switching models for forecasting the time series observations based on stock prices. The Semi-parametric Markov Switching models for these models are a class of popular methods that have been used extensively by researchers to increase the accuracy of fitting processes. The main part of these models is based on kernel and core functions. Despite of existence of many kernel and core functions that are capable in applications for forecasting the stock prices, there is a widely use of Gaussian kernel and exponential core function in these models. But there is a question if other types of kernel and core functions can be used in these models. This paper tries to introduce the other kernel and core functions can be offered for good fitting of the financial data. We first test three popular kernel and four core functions to find the best one and then offer the new strategy of buying and selling stocks by the best selection on these functions for real data.