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Factors Affecting Energy Intensity in Provinces of Iran: Bayesian Averaging Approach
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Maryam Ashouri   , Hojjat Parsa * , Ebrahim Heidari |
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Abstract: (5482 Views) |
| The identification of the most important factors affecting energy intensity with the aim of controlling and managing energy consumption is an important topic. Findings of different empirical studies on the factors affecting energy intensity are inconsistent and this raises uncertainty about the employed models. One of the techniques that conform to these uncertainty conditions of the model is the Bayesian averaging approach. The purpose of this study is to identify robust and fragile factors affecting energy intensity in Iran provinces over the period from 2008 till 2015 using Bayesian averaging approach. The studied variables are selected from among the economic, demographic, industrial, commercial, transportation, Energy sector, factors related to Knowledge-based economy and climate factors. 24 variables were reviewed and by assessment of more than 8 million regressions and Bayesian averaging of the coefficients, 9 variables were identified as the most affecting factors on energy intensity in Iran provinces; share of service sector in production, ratio of export to production, share of oil and petroleum products in energy consumption, income per capita, energy price, number of warm months, per capita capital of employed persons, number of cold months and population growth rate. It was also revealed that per capita income, share of service sector in production, share of oil and petroleum products in energy consumption, energy price and number of warm months have negative effect on energy intensity but other robust variables increase energy intensity. These findings can provide important policy recommendations, especially for the use of energy planners and policy makers. |
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| Keywords: Energy Intensity, Uncertainty, New Bayesian Averaging Approach, Robust and Fragility Factors |
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Full-Text [PDF 1102 kb]
(1066 Downloads)
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Type of Study: Research |
Subject:
Special
Received: 2018/12/25 | Accepted: 2019/04/9 | Published: 2019/05/15
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