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This paper develops a dynamic general equilibrium model (DGEMI) for evaluating energy policies in Iran’s economy. DGEMI provides a detailed multisector framework for analyzing economic transition, removal of energy subsidy, and technological change policies. The results show that eliminating energy subsidies (once- for -all or gradually) in the absence of technological progress is in itself insufficient to stimulate the investment and economic growth. Although the energy intensity along with this policy declines over time, its decline can not be attributed to the energy efficiency, since the economy falls into the lower level of new steady state after removing the energy subsidies. On the other hand, the combination of eliminating energy subsidies and technological progress policies provide a strong growth stimulus accompanied by a pronounced increase in productive efficiency and a decline in intensive energy.

This study investigates the Granger causality relationship between energy consumption, carbon emission and industrial value added, including labor and gross fixed capital formation in the model. We found that energy consumption is Granger cause of carbon dioxide emission and industrial value added. Also results of variance analysis model suggests that the long term effects of variables on their own swings gradually declines, and share of other variables increases.

Importance of renewable-energy resources because of scarcity, greenhouse-gas emissions, and their fundamental roles in production and sustainable development, has made governments to reduce energy consumption and improving energy efficiency. In this regard, targeting subsidies rule was running in Iran since 2010. Since energy additionaly to labor and capital is one of the important inputs in production, running this rule with increasing energy price, affects manufacturing industries due to the type of energy and industry. In this research, the impacts of price liberalization on electricity efficiency have evaluated. Partial adjustment model, generalized method of moment and energy intensity measure are applied to this purpose. Data are collected from Statistical Center of Iran during 1995-2013. The research innovations are: using a partial adjustment model in evaluating efficiency, assessment whole industrial groups, and the period of research. The results show that in 95 percent level of confidence, the Iranian manufacturing industries are significantly flexible for changing the use of electricity. But the electricity efficiency has gotten worse after running the rule. In general, apposite of expectations, targeting subsidies rule has failed to improve electricity efficiency at least in short run.

This paper evaluates the threshold effect of income on carbon dioxide emissions intensity in the MENA countries using panel smooth transition regression model. For this purpose, the variables of financial development, openness, energy intensity, income per capital and carbon dioxide emissions intensity over the period 1980 to 2011 are employed. While the results strongly indicated the existence of a nonlinear relationship, considering one transition function and two threshold parameters is sufficient to specification of nonlinear relationship among variables. The empirical results show that the slope parameter in which the speed of adjustment represent from one regime to another one is estimated equivalent of 78762, and two threshold parameters estimated 1176 $ and 11614 $ based on income per capita respectively. The variables of openness and income per capital lead to reduce carbon dioxide emissions intensity in both regimes in which the impact of income per capital in first regime and openness in second regime is greater than another regime. Although, financial development leads to slight increase in carbon dioxide emissions intensity in the first regime, but in the second regime leads to decreases it.

In this research, By using data envelopment analysis method and output distance functions to decomposes energy productivity change into four components; technical efficiency change, technological change, changes in capital to energy ratio and labor in energy ratio In the industrial sector of the country Iran during the period 2014-2004. To this end, the output-axis data envelope analysis method has been used with the assumption of constant returns to scale. The results show that the effect of changing the ratio of capital to energy is a major factor in reducing the of energy intensity in the industrial sector of the Iran, changes technological progress, changes labor- energy ratio and changes technical efficiency drove up energy intensity in most industries.

Economic growth planning and policy making is one of the macrocosmic goals which it need to pay specific attention to energy and environmental sector and their relationship with production. Therefore, this study has conducted to investigate the relationship between economic growth, energy consumption and environmental pollution using a Spatial Panel Simultaneous-Equations model for 9 developing countries during 2000-2011. Empirical results of this method show that energy consumption, economic growth and environmental pollution in each country is affected by these factors in neighboring countries. The results of research confirm there exists bidirectional causality between energy consumption and environmental pollution, economic growth and environmental pollution. Thus, there is a bidirectional causal relationship between energy consumption and economic growth. Regarding to result of this study suggests to achieve the sustainable economic growth should be used tax tools for controlling the emissions of CO2 and replacement of the renewable energies with fossil fuels.

In spite the global efforts to reduce energy intensity; Iran’s energy intensity has been increasing during the recent decades. To get a more detailed investigation of energy intensity, this study aims at decomposing energy intensity into its components including efficiency and structural change as well as at examining driving forces behind Iran’s energy intensity components during 1973-2011. Energy intensity decomposition showed that efficiency changes accounts for the most of increased energy intensity. It is found in this study that income (GDP), capital- labor ratio and urbanization are the most determinants of energy intensity and its components. Regarding the non-linear relationship between energy intensity and driving forces of income and capital-labor ratio as well as the estimated turning points, income plays a significant role in increase of energy intensity while capital-labor ratio tends to induce a reduction in energy intensity. Although urbanization has a positive contribution to energy intensity via structural changes component, its dominant effect on improved energy efficiency leads to an overall effect of reduced energy intensity by more than 1.8% as 1% increase in urbanization. The results showed a limited effect for price and share of industry in GDP and left no significant role for economic integration and foreign direct investment. The corresponding value for these variables remain less than 0.05%. 1.0pt;line-height:85%;font-family:"B Zar";letter-spacing: -.2pt;mso-bidi-language:FA;mso-ansi-font-style:italic'>کار با شاخص‌های شدت انرژی و نقطه عطف مترتب بر آنها در مجموع اثر درآمد در جهت افزایش شدت انرژی و اثر سرمایه در جهت کاهش شدت انرژی ارزیابی شد. اما شهرنشینی با وجود افزایش شدت انرژی از طریق تغییرات ساختاری از طریق بهبود کارایی در مجموع موجب کاهش شدت انرژی فراتر از 8/1 درصد به ازای 1 درصد افزایش شهرنشینی خواهد شد. اثر قیمت و سهم صنعت از تولید ناخالص داخلی بر شاخص‌های فوق محدود و اثر متغیرهای شاخص ادغام تجاری و سرمایه‌گذاری خارجی قابل اغماض ارزیابی شد. رقم متناظر برای متغیرهای یاد شده بیشتر کمتر از 05/0 درصد به دست آمد.

This study aims at analyzing the energy intensity and also the effect of changes in the production technology on the efficiency of energy consumption in Iranian manufacturing sector. To this end, a regression method entitled the Translog Cost Equation Function is used to evaluate the energy consumption. The period of investigation is 1999- 2011. The results show that the energy intensity in the period of investigation is equal to 0.08 percent which indicates the effectiveness of this variable in the industrial sector. Findings also demonstrate that the technology had the lowest effect, while the small change in the price of energy (i.e. substitution and budgetary effects) had the highest effect on the energy intensity. This means that due to the structure of the industrial sector of the Iranian economy and the low price for energy as well as its adequate supply has led to the utilization of energy intensive components.

Introduction: The final energy consumption per capita in Iran in the agricultural sector is 3.4, as well for household sector is 2, besides the commercial and public sectors are 1.6, and transportation and industry are 1.4 times the global average. This is due to low efficiency in operation, high energy consumption, as well as the use of energy goods and services. The use of renewable energy in the agricultural sector, while increasing the security of energy supply, will reduce global warming, stimulate economic growth, create jobs, and increase per capita income and social justice and environmental protection in all areas. The purpose of this study is to investigate farmers' preferences for using solar energy in Sari.Materials and Methods: The Choice Experiment methods allow researchers to focus on valuing final changes as multidimensional features rather than discrete changes. Choosing between options encourages respondents to examine their preferences in detail related to different management programs. The Choice Experiment test approach consists of several steps, which include designing the Choice Experiment test, determining the sample size and method of data collection, estimation process, and modeling the Choice Experiment test. Designing a Choice Experiment test consists of five important steps which are defining attributes, determining the relevant levels, conducting an experimental design, constructing Choice sets, and measuring preferences. After determining the criteria affecting the prioritization of renewable energy, liketechnical, environmental, economic, social, and political criteria, in order to investigate the willingness to Pay of Sari farmers, a test questionnaire was designed. The criteria obtained from the review of prioritization of renewable energy were considered as the attributes of the Choice Experiment and the price attribute was added to the above criteria. A total of six technical, economic, social, political, environmental, and price attributes were considered to investigate farmers' willingness to pay. In the review of the studies and the current situation, the levels of each of the attributes were determined. To determine the levels of price attribute, these points were considered; the price of agricultural electricity per kilowatt-hour is 383 Rials, which was approximately 400 Rials for the current situation.Results and Discussion: To investigate the farmers' preferences for using solar energy, 98 questionnaires of farmers in Sari were completed in September 2019. Each questionnaire included 8 choice set cards and each card included three options, based on which, the number of observations in Sari is equal to 2352 observations. The purpose of this study is to investigate the preferences of farmers in Sari for the use of solar energy. For this purpose, the Multinomial logit, the Random parameter logit, the latent class, and the Random parameter logit latent class are used. Based on the results of the Multinomial logit method, environmental and price attributes at the level of one percent and economic attribute at the level of five percent are statistically significant, but political, social, and technical attributes are not statistically significant. The Alternative-specific Constants (ASC) in the first and second options are not statistically significant. Based on the results of the Random Parameter Logit estimation method, environmental, economic and price attributes are statistically significant at the level of one percent. Technical, political, and social attributes are not statistically significant, which shows that farmers do not make a significant difference between these two attributes. The Alternative-specific Constants (ASC) are significant in the first option at the level of five percent and the second option at the level of one percent. The results of latent class estimation show that in the first class, environmental, economic, political, social, and price attributes are statistically significant at the level of one percent and technical attribute at the level of ten percent. The Alternative-specific Constants (ASC) are statistically significant at the level of one percent in the first class. In the second class, technical attribute at the level of five percent and environmental attribute at the level of ten percent are significant, besides other attributes in the second class are not statistically significant. The most sensitive class is the first class and farmers of the second class are considered the base class. The results obtained from the Bayesian and Akaike criteria of different classes showed that the two classes have the lowest values of BIC and AIC criteria and the class is appropriate. After determining the appropriate class, the model was estimated. The results of model estimation were calculated by the Latent Class Random Parameter logit method. In the first class, environmental attributes and price are significant at the level of one percent and economical attributes at the level of five percent. Also, the Alternative-specific Constants (ASC) is significant at the level of one percent, but, in the second class, the attributes are not statistically significant. Technical, environmental, economic, political, social, and price attributes, as well as the option of status quo or the Alternative-specific Constants (ASC) in the second class, do not affect farmers' utility due to the lack of statistical significance.Conclusion: A comparison of the results obtained from the four methods shows that the highest value of the estimated coefficient for environmental attributes was in the latent class method and the lowest value was in the multinomial logit method; Comparison of fitted methods shows that the highest Log-likelihood is related to the latent class random parameter logit method and the lowest value is related to the multinomial logit method. Accordingly, the highest value of Akaike and Bayesian criteria is related to the multinomial logit method and the lowest value is related to the latent class random parameter logit method which is better than other methods according to the good fit criterion.

Background and aims: Fossil fuels Emission and their limited resources make to use renewable energy with more sustainable energy sources and less minimal environmental impacts. One of the most appropriate renewable energies considered lots of advantages including being renewable and environmentally friendly and containing social and economical interests, is Biomass. “Biomass” means a power source that is comprised of, but not limited to, combustible residues or gases from forest products manufacturing, waste, byproducts, or products from agricultural and orchard crops, waste or co-products products from livestock and poultry operations, waste or byproducts from and food processing, urban wood waste, municipal solid waste, municipal liquid waste treatment operations, and landfill gas. Due to the wide availability of biomass worldwide, mainly because it can be obtained as a by-product of many industrial and agricultural processes, biomass represents a growing renewable energy source with high growth potential. Biomass helps reduce the amount of GHG that give more impact to global warming and climate change. The biomass emissions level is far smaller compared to fossil fuels. The basic difference between biomass and fossil fuels when it comes to amount of carbon emissions is: all the CO2 which has been absorbed by plant for its growth is going back in the atmosphere during its burning for the production of biomass energy. While the CO2 produced from fossil fuels is going to atmosphere where it increases greenhouse effect. Another great advantage of biomass energy is that it is an indigenous fuel. The fuels from biomass materials can be produced locally and no high technology is required. Producing fuel from biomass materials reduces the dependence of a country on foreign resources for their fuel requirements. Moreover, since this indigenous fuel is labor intensive, it can contribute to the generation of new jobs, particularly in rural and farming communities. The number of employed workers required is 3-6 times greater than the fossil energy production in the associated processes. This study was aimed to identification and green grading of environmental management in that’s jobs. Some other socio-economic benefits can be counted such as slowing down the migration from the rural areas to cities, decreasing the issues associated with rapid urbanization, and developing a biodiesel production industry. Among its great benefits is the forest use of the territory, which would also serve to clean the forest and thus prevent forest fires, and the ability to generate jobs. Biomass generates continuous employment such as the extraction of raw materials from the countryside and the bush. This study was aimed to identification and green grading of environmental management in that’s jobs. The research questions are: 1. What are green job indicators? and 2. has renewable energy biomass business indicators of green jobs? Methods: This study was qualitative – quantitative, first according to the grounded theory qualitative method semi-deep interviewed with 50 environmental experts in the Environmental Protection Agency, the municipality, faculty members of the universities, the natural resources and watershed management, agriculture ministry and NGOs active in the environment conducted a with purposeful sampling (snowball). Regarding qualitative data validation were used constant data comparison, reviewing the observers and handwriting by participants and use of foreign and expert researchers familiar with qualitative research as an observer. Then, data was analyzed using the grounded theory of open, axial and selective coding analyzed in MAXQDA software. Once coding categories emerge, the next step is to link them together in theoretical models around a central category that hold everything together. In order to explain the grounded theory, green jobs are considered as the central variable, and the main line of research is defined using reminders and diagrams around it, and finally the green indicators derived from it are developed. Based on them, researcher-made questionnaire was designed in a combination, closed response with 5-rate likert scale. In order to determine the validity of the questionnaire, the content validity was used with the lawshe model and with reviewing previous studies was determined, the scope of the questionnaire in greenness of the job, and the reliability of the questionnaire was obtained using Cronbachchr('39')s alpha coefficient for internal consistency. Cronbachchr('39')s alpha value for each research question was more than 0.7, the reliability of the questionnaire was approved. Also, the Cronbachchr('39')s alpha coefficient of the questionnaire was 0.890. In order to estimate the repeatability, the retest method and the ICC index were used that index was 0.996 (p <0.001), indicating its high repeatability. For estimating the results of greenness and its degree in the jobs of renewable energy biomass, were used statistical analysis of Kolmogrov-Smirnov test, single-sample t-test and Friedman test in SPSS software. Result: Findings of the qualitative research on the structure of green job identification and prioritization were discussed in six categories including establishment in accordance with the legal and technological infrastructure of the green job as context, green job as a phenomenon, environmental pollution elimination and the health risks reduction of the community as causal conditions, green management as operational strategies, environmental empowerment of jobs as an intermediary conditions and economic and environmental benefits as a consequence. The results of quantitative to showed that jobs studied are considered green jobs and their green grading are as follows: 1. Maintenance (mean=5/61), 2. System Design (mean=4/83), 3. Training (mean=4/22), 4. Quality Monitoring and Quality (mean=4/03), 5. Collection (mean=3/64), 6. Manufacturer (mean=3/61) and 7. Worker and System Administrator (mean=2/06). According to the results, components of green jobs are defined including: (1. explaining Green Jobs, Productivity of Occupations, 2. environmental Protection and Health, 3. Green potentials and incentives, 4. environmental Standards and Indices of Health and Green Management, 5. environmental and health challenges and solving energy crisis with the help of green jobs, 6. environmental education and green culture, 7.environmental empowerment through a variety of environmental and health education, informing and accompanying NGOs, 8. economic-ecological profitability and the optimistic approach to economic interests (green economy) and impact of economic issues, profitability, financial support, market regulation, and return on investment in the process of greening and green expanding businesses). Results show that green indices of occupations are 1- environmental and health of profile occupational, 2- strengths and weaknesses, threats and opportunities green jobs, 3- green supply chain management of businesses, 4- impact of green jobs on sustainable development and community health, 5- effect of environmental education on the green performance of occupations, the impact of environmental advertising on green performance and 6- reduce employee costs and increase business profits through environmental management. These green jobs literature extols the virtues of generating energy using “wood waste and other byproducts, including agricultural byproducts, ethanol, paper pellets, used railroad ties, sludge wood, solid byproducts, and old utility poles. Several waste products are also used in biomass, including landfill gas, digester gas, municipal solid waste, and methane. Conclusion: The green features of the biomass business are included solving the problem of fossil fuels, caused by fossil energy and renewable energy sources. identification and green grading jobs diversifying energy sources, sustainable development, securing energy, removing environmental and health problems would help to managers and policy makers for identifying and providing executive solutions and identifying multifaceted priorities for green management. Despite the high potential of bio-economy in renewable energy (biomass) and high amounts of raw materials in the agricultural waste and sewage has not been fully realized. To achieve of developing a competitive economy, low-carbon resources with efficient resources, global economic markets have shifted strategy towards renewable energies, so as to create green jobs in order to reduce environmental problems (waste and climate change). For performance of macroeconomic policy in notification Supreme Leader on the restructuring of the countrychr('39')s economic structure has proposed policies to change reducing dependencies on fossil fuels and external resources towards the creation and development of green jobs in the field of renewable energy, especially biomass, because there are a lot of raw materials in the country, especially in the villages and without necessary to high technologies. Biomass development increase energy efficiency, the use of renewable energy resources and the creation of a favourable environment for investment in energy efficiency measures and the generation of ‘green’ jobs. The rural development prospects for green job growth are mixed. Rural areas contain biomass feedstocks which will be increasingly relied upon to offset fossil fuel dependencies. The distribution of those feedstocks, however, is not uniform across rural areas. Furthermore, the technologies to convert those feedstocks into fuels and other uses are yet to be demonstrated at commercially successful scales. Both policy development and research activities should be focused on the efficient utilization of rural natural resources, human capital, and rural infrastructure in achieving national green policies. The green economy appears to be fertile ground for unbiased, academic research to examine some of the regional consequences of green jobs growth and green jobs policies, to include an examination of rural opportunities, but going well beyond that dimension to include the integration of statewide and multi-state regional development opportunities as well as consequences. This study was not about raw materials (waste and sewage) to produce renewable energy biomass, and it is possible that this section may also be effective in the creation and development of green jobs, then there may be restrictions on the generalized findings, interpretations, and attributes of the causation of variables. Therefore, it is suggested that future research into this part of the process of producing renewable energy biomass should be considered.