Artikel
Forecasting of CO2 emissions in Iran based on time series and regression analysis
Iran has become one of the most CO2 emitting countries during the last decades. The country ranks after Japan and Germany in terms of CO2 emissions. However, from an economic viewpoint, the gross domestic product (GDP) of Iran is lower than the summation of Berlin and Tokyo GDP. Moreover, a large proportion of Iran's revenue comes from the crude oil export; therefore, this level of CO2 emission cannot be economically driven and is as a result of high energy intensity in this country. This is while the government also has not a clear program in this regard. The Sixth Five-year Development Plan of Iran, in addition, sets a number of ambitious targets mostly regarding the energy intensity, GDP growth, and renewable energies, but does not mention to CO2 emission issue. Therefore, prospects for an early settlement of the dispute are seemingly dim. Our aim is to predict Iran's CO2 emissions in 2030 under assumptions of two scenarios, i.e. business as usual (BAU) and the Sixth Development Plan (SDP), using multiple linear regression (MLR) and multiple polynomial regression (MPR) analysis. Findings suggest that Iran most likely will not meet its commitment to the Paris Agreement under the BAU's assumptions; however, full implementation of the ambitiously shaped SDP could have met the target by end 2018.
- Language
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Englisch
- Bibliographic citation
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Journal: Energy Reports ; ISSN: 2352-4847 ; Volume: 5 ; Year: 2019 ; Pages: 619-631 ; Amsterdam: Elsevier
- Classification
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Wirtschaft
- Subject
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CO emission
Energy
Paris agreement
Regression
Scenario
- Event
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Geistige Schöpfung
- (who)
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Hosseini, Seyed Mohsen
Saifoddin, Amirali
Shirmohammadi, Reza
Aslani, Alireza
- Event
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Veröffentlichung
- (who)
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Elsevier
- (where)
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Amsterdam
- (when)
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2019
- DOI
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doi:10.1016/j.egyr.2019.05.004
- Handle
- Last update
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10.03.2025, 11:41 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Hosseini, Seyed Mohsen
- Saifoddin, Amirali
- Shirmohammadi, Reza
- Aslani, Alireza
- Elsevier
Time of origin
- 2019
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