Historical emissions for the years 1995, 2000, 2005 and 2010 are taken from the data reported to the UNFCCC GHG inventory database (UNFCCC, 2015). We interpolated missing time series values. PRIMAP data is used to calculate growth rates in order to calculate historical emissions for the period from 1990 to 1994 as well as to expand the historical time series to 2017, excluding LULUCF (Gütschow et al., 2019).
NDC and other targets
Data for the NDC business-as-usual scenario is based on the country’s Second National Communication for the years 2015 and 2030 (MENR, 2017a). We interpolated missing time series values. As there is no gas-by-gas split available for the years 2015 and 2030, we have calculated a country-specific historical ratio between SAR and AR4 by recalculating the historical years for which a gas-by-gas spilt is available. We consider the years 1995, 2000, 2005 and 2010 to calculate the average ratio (hereafter referred to as average conversion factor). Because of disproportionate deviation, the year 1994 was not considered for the average conversion factor. The LULUCF sector was not considered either for calculating the average conversion factor as we assume CO2 to be the main GHG in this sector.
Data for the NDC target is based on the country’s Second National Communication and its NDC (MENR, 2017b). In order to exclude LULUCF from the NDC target, we have calculated the sector shares in the overall emissions reductions target of 30% by 2030. The LULUCF sector contributes the largest share (47%) to the overall emissions reductions target, with all the remaining sectors together contributing 53%. The projected NDC BAU by 2030, excluding LULUCF, was first multiplied by 30% and then by 53% in order to get the adjusted NDC target, without considering mitigation actions in the LULUCF sector.
Current policy projections
Upper range (CPP Max.)
In January 2017, the Ministry of Environment and Forestry released an assessment report containing an updated emissions baseline to inform how sectors could contribute to meeting Kenya’s NDC. The objective of the assessment was to examine expected contributions to emissions reductions from each sector, explore sectoral mitigation actions to meet the NDC target, and examine new information that could impact the achievement of the NDC targets. The update was based on new historical activity data for each emission source as well as new information on drivers of future emissions (2016 – 2030), such as GDP growth forecasts by sector, new projections on electricity generation projects and new forecasts for urbanisation (MENR, 2017a).
We have taken the data from this updated BAU as the upper bound of our current policy scenario and harmonised it to the last historic year (2017) (MENR, 2017a; Republic of Kenya, 2015).
Lower range (CPP Min.)
For the lower end of the range, we have taken the updated BAU for all sectors except electricity generation, transport and agriculture. The data for electricity generation is taken from the 2017-2037 Least Cost Power Development Plan (LCPDP), for the transport sector, the data is taken from the 2018/2019 Transport Sector Climate Change Annual Report, which indicates a list of mitigation actions the sector plans to implement and that should allow it to achieve its sectoral NDC target, and for the agriculture sector, the data is taken from the Kenyan Climate Smart Agriculture Strategy 2017-2026, which includes an indicative 2026 emission levels based on proposed mitigation actions (Government of Kenya, 2019; Ministry of Agriculture, 2018; Republic of Kenya, 2018).
This scenario is also harmonised to the last historic year.
We applied a novel method to estimate the COVID-19 related dip in greenhouse gas emissions in 2020 and the trajectory to 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 first update the current policy projections using most recent projections. We then distil the emission intensity (GHG emissions/GDP) from this pre-pandemic scenario and apply to it most recent GDP projections that take into account the effect of the pandemic. To capture a wide range, we use more than one GDP projection and combine data from international sources. For Kenya, we consider the projections from IMF, World Bank, the African Development Bank and the National Treasury of Kenya (World Bank, 2020). The Kenyan National Treasury projects domestic economic growth of 2.5% in 2020, down from 5.4% in 2019 and compared to pre-COVID-19 estimates of 6% annual growth in 2020 (MyGov, 2020). These projections are slightly more optimistic compared to estimates from the African Development Bank and the International Monetary Fund (IMF), that project growth rates for 2020 of 0.6% or 1%, respectively (AFDB, 2020; IMF, 2020). The Government of Kenya and the IMF predict GDP growth in 2021 to be almost at pre-COVID-19 levels with an annual percentage change of 5.8% and 4.7%, respectively (IMF, 2020; MyGov, 2020). Since the most recent GDP projections only provide values for the next few years (up to 2025), we used the same GDP growth rate as pre-pandemic current policy scenario for the remaining years.
Global Warming Potential values
The CAT uses Global Warming Potential (GWP) values from the IPCC’s Fourth Assessment Report (AR4) for all its figures and time series. Kenya uses GWP values from the IPCC’s Second Assessment Report (SAR), therefore we converted all figures and time series from SAR to AR4 GWP values.