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Energy subsidies can incentivise the overconsumption of energy resources and contribute to other economic or social distortions. In this paper, an ex-post analysis is presented that explores the extent to which electricity subsidy reform could have reduced Iran's energy demand during the period 1984–2017. It also quantifies the techno-economic and environmental benefits that could have been achieved through such reforms. A time-varying econometric model is linked to an energy systems optimisation model. The former estimates electricity demand under different subsidy removal scenarios, and the latter identifies the cost-optimal generation mix to meet the demand. The results of cost-optimal transition pathways under subsidy removal scenarios are compared with the real-world energy system development during the study horizon. The comparison reveals that the subsidy reform could have reduced the total cumulative electricity consumption by 22%. Renewable share in power generation could have increased from 5% to 15%. Moreover, the reform combined with a cost-optimal generation pathway would have saved $69 billion and avoided 944 million tons of CO 2 emissions. The analysis also shows that every five-year delay in subsidy removal causes about 100 million tons of additional CO 2 emissions. Finally, the paper presents lessons learnt for future energy modelling. • An ex-post analysis quantifies the benefits of energy subsidy reform in the power sector. • The total cumulative electricity consumption could have been reduced by 22%. • Renewable share in power generation could have tripled. • Subsidy reforms combined with cost-optimal generation could have saved $69 billion. • The paper presents lessons learnt for future energy modelling. [ABSTRACT FROM AUTHOR]

Energy subsidies can incentivise the overconsumption of energy resources and contribute to other economic or social distortions. In this paper, an ex-post analysis is presented that explores the extent to which electricity subsidy reform could have reduced Iran's energy demand during the period 1984–2017. It also quantifies the techno-economic and environmental benefits that could have been achieved through such reforms. A time-varying econometric model is linked to an energy systems optimisation model. The former estimates electricity demand under different subsidy removal scenarios, and the latter identifies the cost-optimal generation mix to meet the demand. The results of cost-optimal transition pathways under subsidy removal scenarios are compared with the real-world energy system development during the study horizon. The comparison reveals that the subsidy reform could have reduced the total cumulative electricity consumption by 22%. Renewable share in power generation could have increased from 5% to 15%. Moreover, the reform combined with a cost-optimal generation pathway would have saved $69 billion and avoided 944 million tons of CO 2 emissions. The analysis also shows that every five-year delay in subsidy removal causes about 100 million tons of additional CO 2 emissions. Finally, the paper presents lessons learnt for future energy modelling. • An ex-post analysis quantifies the benefits of energy subsidy reform in the power sector. • The total cumulative electricity consumption could have been reduced by 22%. • Renewable share in power generation could have tripled. • Subsidy reforms combined with cost-optimal generation could have saved $69 billion. • The paper presents lessons learnt for future energy modelling. [ABSTRACT FROM AUTHOR]

Abstract: Since the beginning of the last century, energy affairs have always been taken into global consideration due to their effects on economies, policies, security and the environment. Fossil fuels have been the first recourse for global energy supply needs (80% of total energy consumption); nonetheless, owing to the production of greenhouse gases, they are gradually being replaced with renewable energy resources. In addition, renewable energy resources are perpetual, unlike fossil fuels, which may be exhausted in less than one century. From 2005 to 2010 (coinciding with Iran's fourth development program), renewable energy resources enjoyed a double-digit global growth rate as a result of environmentally friendly perception and a reduction in equipment price. Some policies and strategies were globally adopted to support renewable energy resources (Feed-In-Tariff, subsidies, etc.). Iran began utilizing renewable resources from the early 1990s and set targets and policies for renewable energy resource utilization in the fourth development program. However, Iran's trend has not aligned with global trends, and it appears that its enormous fossil fuel reserves overshadow the growth of renewable energy resources in the country. In this paper, the status, achievements and policies of Iran during the fourth development program will be discussed and compared with global trends. [Copyright &y& Elsevier]

Energy has a strategic role in the economic and social development of countries. In the last few decades, energy demand has been increasing exponentially across the world, and predicting energy demand has become one of the main concerns in many countries. The residential and commercial sectors constitute about 34.7% of global energy consumption. Anticipating energy demand in these sectors will help governments to supply energy sources and to develop their sustainable energy plans such as using renewable and non-renewable energy potentials for the development of a secure and environmentally friendly energy system. Modeling energy consumption in the residential and commercial sectors enables identification of the influential economic, social, and technological factors, resulting in a secure level of energy supply. In this paper, we forecast residential and commercial energy demands in Iran using three different machine learning methods, including multiple linear regression, logarithmic multiple linear regression methods, and nonlinear autoregressive with exogenous input artificial neural networks. These models are developed based on several factors, including the share of renewable energy sources in final energy consumption, gross domestic production, population, natural gas price, and the electricity price. According to the results of the three machine learning methods applied in our study, by 2040, Iranian residential and commercial energy consumption will be 76.97, 96.42 and 128.09 Mtoe, respectively. Results show that Iran must develop and implement new policies to increase the share of renewable energy supply in final energy consumption. [ABSTRACT FROM AUTHOR]