Overall rating
Almost Sufficient
Policies & action
1.5°C Paris Agreement compatible
< 1.5°C World
Internationally supported target
Critically insufficient
4°C+ World
Fair share target
1.5°C Paris Agreement compatible
< 1.5°C World
Climate finance
Not applicable
Net zero target

Comprehensiveness evaluated as

No target
Land use & forestry

impact on overall emissions is


Historical emissions

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

We calculate the unconditional contribution to be equivalent to an unconditional target of 7% below BAU, including LULUCF – or 4% below BAU, excluding LULUCF emissions. This corresponds to emissions levels of 126 MtCO2e by 2030 (expressed in AR4 values) (see further assumptions on GWP below).

In its first NDC, Kenya included a separate target of 47% for the LULUCF sector. While explicit reference to this target has been dropped in the update, we assume that Kenya will meet about half of its NDC with reductions in land sector emissions.

Conditional target: The NDC states that Kenya will bear 21% of the estimated required mitigation costs itself. In the absence of information on sectoral mitigation costs, we assume that this 21% financial contribution is equivalent to a 21% share of the emission reductions achieved. On that basis, emissions in 2030 under Kenya’s unconditional target would be 126MtCO2e, around a 4% reduction below BAU (excl. LULUCF).

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.

COVID-19 impact

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 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).

Kenya has indicated that it will account for its NDC using GWP values from the IPCC’s fifth assessment report (AR5). However, we assume that its reference to a BAU of 143 MtCO2e is expressed in GWP values from the IPCC’s second assessment report (SAR). Similar to the first NDC, the updated NDC quantified the 2030 reference value based on the 2030 GHG emissions projected in the National Climate Change Action Plan (2013-2017) and values in that report are in SAR. For the purpose of this analysis, we assume that the sector contribution to achieving the target and LULUCF assumptions used in the CAT country assessment remain unchanged and we converted the IPCC SAR GWP values to AR4 GWP values. Converting from SAR to AR5 under these assumptions would increase the targets by a couple of MtCO2e. If, however, the BAU is already in AR5 values, then the targets will be lower than the estimates provided here.

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