Historical emissions data (excluding LULUCF) until 2018 is based on the PRIMAP dataset. LULUCF data for the years 1994 and 2000 are taken from the UNFCCC emissions inventory, and the 2010 data is from the Third National Communication (Department of Environment of Iran, 2017).
For the years 2019 and 2020, we have used the following approach to estimate CO2 and non-CO2 emissions:
- CO2 emissions are estimated using growth rates from the Global Carbon Budget data (Global Carbon Project, 2020), applying these to 2018 historical data.
- For non-CO2 emissions in the energy sector, we have assumed the same growth rates as for CO2 data.
- For non-CO2 emissions in other sectors, we extrapolate the last 5-year average from historical emissions, as we assume the impact of the pandemic to be minimal for sectors such as agriculture or waste.
INDC and other targets
Iran’s INDC is expressed as a percentage reduction below business-as-usual (BAU) emissions in 2030, but without specifying what the BAU scenario is. In our calculations we have used data points for the 2025 BAU provided in the Third National Communication released in 2017. For some sectors, the BAU data is not directly provided. For those sectors, we have used the ‘Mitigation Scenario’ emissions levels and have added to them the mitigation potentials given in the Third National Communication. We have excluded BAU emissions from LULUCF.
As the Third National Communication only provides BAU values for 2025, we have interpolated for values between 2010 and 2025. For the 2010 base year data, we take the values communicated in the Third National Communication.
The data in the Third National Communication assumes Global Warming Potential (GWP) values for non-CO2 greenhouses gases from the IPCC’s Second Assessment Report (SAR). As the CAT uses GWP values from the Fourth Assessment Report (AR4), values from the Third National Communication have been converted from SAR to AR4 using a conversion factor based on PRIMAP data.
Current policy projections
There is a high level of uncertainty regarding the emissions development in Iran. This is mostly due to uncertain economic growth projections linked to international economic sanctions and the COVID-19 crisis. We capture this uncertainty by presenting a range for the current policy projections based on historical trends or mitigation scenario trends for agriculture and waste emissions, and GDP elasticity for energy and industry.
For the emissions in the agriculture and waste sector, we apply trends from the 2025 Mitigation Scenario provided in the Third National Communication and harmonise these to historical data. In the years 2025–2025, we extrapolate using the 2010–2025 data trend. For the energy and industry sector emissions, the projections are based on the GDP elasticity of energy and industry GHG emissions assumed in the 2025 Mitigation Scenario, which we adjusted for to reflect more recent GDP growth estimates from the IMF (2021c).
Measures in the mitigation scenario include improving energy efficiency, fuel switch from oil to gas in industry, as well as the residential and commercial sectors, development of rail and public transport, gas flaring reduction, development of renewable and nuclear energy, etc.
For the emissions in the agriculture and waste sector, we assume a five-year trend based on 2014–2018 emissions. For the energy and emissions sector emissions, the projections are based on historical trends of GDP elasticity of energy and industry GHG emissions between 1990 and 2020, using the latest historical GHG emissions data series, and by applying GDP growth estimates from IMF until 2030.
Global Warming Potentials Values
The CAT uses Global Warming Potential (GWP) values from the IPCC's Fourth Assessment Report (AR4) for all its figures and time series. Assessments completed prior to December 2018 (COP24) used GWP values from the Second Assessment Report (SAR). Iran’s Third National Communication, from which much of our data is derived, uses SAR GWP values. Values from the Third National Communication have been converted from SAR to AR4 using a conversion factor based on PRIMAP data.