UAE

Historical emissions for the year 1994, 2000, 2005 and 2014 are taken from the UNFCCC GHG Inventory database (UNFCCC, n.d.). More detail on the 2014 values are available in UAE’s fourth National Communication (Government of the United Arab Emirates, 2019b). The UNFCCC dataset contains two errors, one for the year 1994 where agricultural emissions are 54.5 MtCO₂e/year in the UNFCCC database while they are 1.8 MtCO₂e/year in the 3rd National Communication (Ministry of Energy, 2012), and one for 2005, where fugitive emissions from the energy sector are double counted, once as methane emissions and once as carbon dioxide emissions (each 21.0 MtCO₂e). We corrected the values using the data from the 3rd National Communication.

We linearly interpolate the data between 1994, 2000, 2005 and 2014. For the years before 1994, we linearly expand the trend of the years 1994 – 2005.

LULUCF

The target in the second NDC includes LULUCF emissions. To estimate the emissions level resulting from target excluding LULUCF, we assumed constant LULUCF emissions between the latest data point available (2014) and the NDC target year (2030).

Global Warming Potential (GWP) values

The GWP values used are not specified in the second NDC. We have assumed that GWPs from the IPCC’s Second Assessment Report (SAR) are used, in line with previous documents submitted to the UNFCCC. We have converted the 2030 BAU value from SAR to AR4 using a conversion factor for the last available data point (2014).

For the current policy projections, we use the following data sources:

• CO₂ from fuel combustion: “Renewable Energy Prospects – United Arab Emirates” (Masdar Institute/IRENA, 2015).
• CO₂ process emissions: Own assumption to continue linear trend based on total industrial processes emissions from the years 1994 to 2014 until 2030.
• Non-CO₂ emissions: Global Anthropogenic Non-CO₂ Greenhouse Gas Emissions: 1990 - 2030 (US EPA, 2019).

We apply the growth rates of those datasets to our historical data for the year 2014 and sum them.

Two policies are not included in the Masdar/IRENA report: the Energy Strategy for 2050 (WAM, 2017) and the deregulation of prices of gasoline and electricity, which were previously subsidised (IEA, 2015). We consider the Energy Strategy a planned (rather than implemented) policy, and thus do not include the targets in the current policy projections.

The deregulation of fossil fuel prices is included in the current policy projections. To estimate the impact of the fuel subsidies-phase out, we take into account an IISD report that calculates the impacts of subsidy phase-out in the UAE and other countries. This report states that national energy related emissions in the UAE would decrease by 14% in 2020 and 12% in 2025 if all subsidies were taken away (Merrill et al., 2015). It is unclear how the reductions relate to the different energy carriers, we thus present a range:

• Lower end: We subtract this amount from our own estimate of energy-related CO₂ emissions for 2020 and 2025, and assume for 2030 that there is a linear trend in the reduction rate as given by Merrill et al. (2015).
• Upper end: We apply the percentage given only to emissions related to oil consumption.

We assume that there is no impact on other gases.

To estimate the potential impact of the Energy Strategy (considered a planned policy), we take the following approach:

• Determine shares of energy carriers in electricity generation up to 2030: Linear interpolation of shares for different energy carriers between last available year in IEA statistics (IEA, 2016) and 2050 targets.
• Apply emission factors of electricity generation: Take last available year from IEA statistics and assume 1% autonomous improvement per year.
• Determine total electricity generation up to 2030: Use values from Masdar/IRENA report (Masdar Institute/IRENA, 2015). To reflect energy efficiency target we assume that efficiency improvements are equal for all energy carriers, meaning that electricity is reduced at the same rate as others. This would not work for deep decarbonisation pathways, but the targets do not reflect that,
  • Efficiency is measured in total final energy consumption per GDP, and
  • Electricity generation equals consumption.

We applied a novel method to estimate the COVID-19 related dip in greenhouse gas emissions in 2020 and its impact until 2030. The uncertainty surrounding the severity and length of the pandemic creates a new level of uncertainty for current and future greenhouse gas emissions. We distil the emission intensity (GHG emissions/GDP) from the pre-pandemic current policy projections and applied it to the most recent GDP projections that account for the effect of the pandemic.

A range of estimates from the International Monetary Fund out to 2025 and the World Bank out to 2021 exists to estimate the potential impact of the pandemic on GDP (IMF, 2020b; World Bank, 2020). We present the range of emissions scenarios in our graph based on maximum and minimum values for total GDP for each year using the upper and lower bounds of the range of GDP projections.

We use the pre-COVID-19 growth rates from the SSP2 scenario for the UAE of the Shared Socioeconomic Pathways (SSP) database to extend the projections to 2030 (Fricko et al., 2017). This scenario has been chosen as it represents a “middle of the road” evolution of future societal developments.

The CAT uses Global Warming Potential (GWP) values from the IPCC's Fourth Assessment Report (AR4) for all its figures and time series.