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Showing 3 results for Energy Subsidy

Dr Ahmad Ameli,
Volume 2, Issue 3 (3-2011)
Abstract

This article seeks to modeling social welfare functions, for assessment of how distribution of transfer payment among socio-economic levels. We consider providing social welfare functions two scenarios, first the each socio-economic levels receives amount of transfer payment equal to others, and second the each socio-economic levels receives that with weighted preferences. The four basic functions determine optimal value of how distribution, and then calculate actual value of that by transforming COICOP to ISIC . Finally the difference between optimal and actual values is determined for rural and urban society and for first and second scenario. At the first scenario the difference between optimal and actual value is smaller than second and this difference at rural society is greater than urban society. The other hand the welfare distribution at the former is worse than later.
Dr Ahmad Ameli, Dr Mehdi Sadeghi Shahdani ,
Volume 4, Issue 11 (3-2013)
Abstract

This paper presents an AHP and FLP model for the allocation of energy subsidies to different economic sectors. To do so, we defined a group of socio-economic criteria that may affected by the allocation of energy subsidies. These criteria are: economic growth, energy intensity, labor intensity, inflation, social cost of air pollutions and distribution of energy subsidy among socio-economic levels. According to calculated weights, we determined the priority of the above mentioned criteria. Also, according to the optimum overall rank of economic sectors, the commercial sector has the highest rank followed by industrial, agricultural and household and transportation sectors. After determining the final coefficients of AHP approach, we determined the allocation of energy subsidies using linier programming approach. We also considerd the change in technology and consumption patterns of household and transportation sectors. Results show that the share of energy subsidies allocated to commercial and transportation sectors should increase to 30.4 and 28.6 percent respectively.
Mr Hossein Hafezi, Mr Siab Mamipour,
Volume 13, Issue 49 (12-2022)
Abstract

Climate change has emerged as a significant global challenge, with its impact increasing rapidly in recent decades. The consumption of fossil fuels, which leads to the emission of greenhouse gases like CO2, is a major contributor to climate change. Iran, ranked as the sixth most polluted country in the world, emitted a staggering 745 million tons of CO2 in 2020. Notably, the power plants sector in Iran accounts for roughly 30% of its total carbon emissions. As a result, the main objective of this paper is to engage in long-term planning for electricity supply and demand in Iran, aiming to reduce carbon emissions in line with the country's obligations under the Paris Agreement. To achieve this goal, we utilized the MESSAGE model to design an electricity generation system that takes into account the potential of renewable sources from 2021 to 2050. Additionally, the ARDL model was employed to estimate electricity demand under various scenarios, including subsidy reforms. These predictions were then incorporated into the long-term planning process for Iran's electricity supply system. The findings of the ARDL model highlight that the subsidy reform strategy leads to a 10% decrease in electricity demand throughout the planning period, indicating effective control over the demand side. On the other hand, the MESSAGE model's findings reveal that Iran's ability to fulfill its responsibilities under the Paris Agreement heavily relies on the utilization of renewable potentials across different regions in power supply planning. While carbon dioxide emissions in Iran's electrical sector are not expected to be reduced in the near future (2020 to 2030). However, in the long term (2040 to 2050), significant reductions in CO2 emissions can be achieved. According to the findings, if the electricity system in Iran is designed in accordance with a chosen scenario that incorporates green technologies and subsidy reforms, the share of renewable technologies can increase from 6% in 2020 to 15%, 50%, and 78% in 2030, 2040, and 2050, respectively. Consequently, carbon emissions in the power generation sector can be reduced by 20% and 54% in 2040 and 2050, respectively, compared to 2020 levels.


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