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ID: 126841

Climate friendly technology transfer in the energy sector: a case study of Iran / Talaei, Alireza; Ahadi, Mohammad Sadegh; Maghsoudy, Soroush

Ahadi, Mohammad Sadegh	Journal Article
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Publication

2014.

Summary/Abstract

The energy sector is the biggest contributor of anthropogenic emissions of greenhouse gases into the atmosphere in Iran. However, abundant potential for implementing low-carbon technologies offers considerable emissions mitigation potential in this sector, and technology transfer is expected to play an important role in the widespread roll-out of these technologies. In the current work, globally existing low-carbon energy technologies that are compatible with the energy sector of Iran are identified and then prioritised against different criteria (i.e. Multi Criteria Decision Analysis). Results of technology prioritisation and a comprehensive literature review were then applied to conduct a SWOT analysis and develop a policy package aiming at facilitating the transfer of low carbon technologies to the country. Results of technology prioritisation suggest that the transport, oil and gas and electricity sectors are the highest priority sectors from technological needs perspective. In the policy package, while fuel price reform and environmental regulations are categorised as high priority policies, information campaigns and development of human resources are considered to have moderate effects on the process of technology transfer.

Key Words

Energy System ; Policy Analysis ; SWOT Analysis ; Low - Carbon Technology Transfer ; Technology Needs Assessment ; MCDM

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ID: 133186

Predicting oil price movements: a dynamic Artificial Neural Network approach / Godarzi, Ali Abbasi; Amiri, Rohollah Madadi; Talaei, Alireza; Jamasb, Tooraj

Jamasb, Tooraj	Journal Article
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Publication

2014.

Summary/Abstract

Price of oil is important for the economies of oil exporting and oil importing countries alike. Therefore, insight into the likely future behaviour and patterns of oil prices can improve economic planning and reduce the impacts of oil market fluctuations. This paper aims to improve the application of Artificial Neural Network (ANN) techniques to prediction of oil price. We develop a dynamic Nonlinear Auto Regressive model with eXogenous input (NARX) as a form of ANN to account for the time factor. We estimate the model using macroeconomic data from OECD countries. In order to compare the results, we develop time series and ANN static models. We then use the output of time series model to develop a NARX model. The NARX model is trained with historical data from 1974 to 2004 and the results are verified with data from 2005 to 2009. The results show that NARX model is more accurate than time series and static ANN models in predicting oil prices in general as well as in predicting the occurrence of oil price shocks.

Key Words

Oil ; Economics ; Oil price forecasting ; Time series model ; NARX model ; Artificial Neural Model ;
Energy Market Pricing

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