Kenya s Nationally Determined Contribution (NDC)

Transcription

1 Republic of Kenya Ministry of Environment and Natural Resources Kenya s Nationally Determined Contribution (NDC) Update of Kenya s Emission Projections and Impact on NDC Target January 2017 Developed with the support of the StARCK+ Climate Change Technical Assistance to the Government of Kenya

2 Republic of Kenya Ministry of Environment and Natural Resources The Principal Secretary, State Department of Environment and officials from the National Climate Change Directorate, Ministry of Environment and Natural Resources, provided guidance and leadership for the preparation of this document. Contact: Principal Secretary, The development of this document was funded by UK Aid from the Government of the United Kingdom through the Technical Assistance to the Government of Kenya Component of the Strengthening Adaptation and Resilience to Climate Change in Kenya Plus (StARCK+) Programme; however, the views expressed do not necessarily reflect the views of the UK Government. For further information contact the consulting consortium of Development Alternatives Incorporated (DAI), Matrix Development Consultants and the International Institute for Sustainable Development (IISD) at Technical Assistance to the Government of Kenya

3 Table of Contents 9.1 Introduction Update of Emission Projection Setting Sectoral Targets against the Updated Emission Projection... 6 Endnotes... 8

4 Abbreviations BAU CO 2 CO 2e GDP GESIP GHG GWh IPCC LULUCF MENR Mt MW NCCAP NDC SLEEK SNC StARCK+ UNFCCC business as usual carbon dioxide carbon dioxide equivalent gross domestic product Green Economy Strategy and Implementation Plan greenhouse gas Gigawatt hour Intergovernmental Panel on Climate Change land use land-use change and forestry Ministry of Environment and Natural Resources million tonnes Megawatt National Climate Change Action Plan Nationally Determined Contribution System for Land-based Emissions Estimation in Kenya Second National Communication Strengthening Adaptation and Resilience to Climate Change in Kenya Plus United Nations Framework Convention on Climate Change Update of Emission Projections

5 Update of Emission Projections and Impact on NDC Target 9.1 Introduction Kenya submitted its Intended Nationally Determined Contribution as its Nationally Determined Contribution (NDC) on 28 th December 2016, when it deposited its instrument of ratification for the Paris Agreement under the United Nations Framework Convention on Climate Change (UNFCCC). 1 Kenya s NDC sets out an ambitious mitigation contribution of abating greenhouse gas (GHG) emissions by 30% by 2030 relative to the business as usual (BAU) scenario of 143 MtCO 2e. The NDC is subject to international support in the form of finance, investment, technology development and transfer, and capacity building. The analysis underlying the mitigation NDC was based on work undertaken for the National Climate Change Action Plan, (NCCAP). 2 This work employed a base year of 2010 when Kenya s total greenhouse gas emissions were determined to be 73 MtCO 2e. Further work on the Second National Communication (SNC) completed in 2014/15 determined that GHG emissions were 70 MtCO 2e in the base year of 2010, but were still expected to rise to 142 MtCO 2e in 2030 consistent with the NCCAP projection. 3 The Ministry of Environment and Natural Resources (MENR) is working with the support of the Strengthening Adaptation and Resilience to Climate Change in Kenya Plus (StARCK+) programme s Technical Assistance to the Government of Kenya to undertake a deeper analysis of the major mitigation sectors (energy demand; electricity generation; transportation; agriculture; land use land-use change and forestry [LULUCF]; industrial processes and waste). This assessment is required to examine expected contributions to emission reductions from each sector, explore options to meet the NDC target, and examine new information that could impact the meeting of the targets. The sectors identified are based on emission source categories of the Intergovernmental Panel on Climate Change (IPCC). Part of this exercise was to update the existing baseline greenhouse gas projection to determine the situation in 2016 and whether there are significant changes in the drivers of emissions (such as: fuel consumption patterns, economic growth, energy efficiency, regulatory policies), that have changed Kenya s outlook on future emissions. This new data includes several years of activity data ( ) and new economic forecasts that impact the emissions projections of all sectors. The purpose is not to change or update the existing BAU scenario or the overall target of 30% emission reductions by 2030, but simply to inform emission reduction strategies in each sector. 9.2 Update of Emission Projection The original BAU or baseline emission projection was created to reflect only existing policies, regulations and financial commitments at the time and did not account for potential new policies or account for climate financing after it was published. At the time it was the most realistic projection of the future given what was known about planned private and public investment. However, since the NDC was published a great deal more information has become available that can be used to update the emission baseline projection and the mitigation option analysis. The NDC was prepared using historical activity data and projection data that was almost exclusively published before the year Update of Emission Projections 1

6 The first step to updating the emission baseline projection for this project was to consider new historical activity data available for each emission source (such as tonnes of cement produced or gasoline fuel consumption for transportation in 2013, 2014 and 2015). Even short term changes in activity levels can significantly alter long-term projections. By gathering the most recent data available from many different sources, activity data was updated until the end of the 2015 year. The result of this update is that, although the overall projection of BAU emissions in 2030 remains roughly the same, 143 MtCO 2e, there are some significant sectoral changes. Table 9.1 overviews some of the sectoral changes to the BAU emissions projection based on changes in historical activity data for the year 2015 as well as the projection in Table 9.1: Changes to Emissions Projections based on New Historical Activity Data (2013 to 2015) Sector SNC BAU Revised % Change SNC BAU Revised % Change Energy Demand % % Transportation % % Electricity Generation % % Industrial Processes % % Agriculture % % Waste % % LULUCF % % TOTAL % % The second step is to consider new information available on the drivers of future emissions (in this case 2016 to 2030). This data had an even greater impact on the emission projection. New GDP growth forecasts by sector, new projections on electricity generation projects and new forecasts for urbanization impact the rate at which almost all emission sources grow over the period. Taking these projection factors into account, along with the changes in historical activity data shown in Table 9.1, reduces the estimated emissions significantly in 2030, from 143 MtCO 2e to 124 MtCO 2e. Table 9.2 details the sectoral changes in emissions due to both historical activity data and changes to emission projection drivers. The overall change in emissions is significant, a reduction in overall emissions in 2030 of almost 14% from the original BAU emission baseline projection that was used for the NDC analysis. The change is primarily driven by the Ministry of Energy and Petroleum s new Master Long Term Plan forecast in the electricity sector. 4 In this updated electricity plan the forecast for electricity generation in 2030 is dramatically lower than in the 2011 Updated Least Cost Development Plan that was used to project emissions for the NCCAP and SNC. 5 A comparison between the Vision Expansion Scenario in the new Master Long Term Plan and the reference case scenario of the old ULCDP indicates a total electricity generation forecast in 2030 of 38,463 Gigawatt hour (GWh) versus the old reference case forecast of 91,946 GWh. This is almost a 40% drop in electricity generation in 2030, and because this demand was largely met Update of Emission Projections 2

7 Table 9.2: Changes to Emissions Projections based on New Historical Activity Data and Emission Projection Drivers Sector SNC BAU Revised % Change SNC BAU Revised % Change Energy Demand % % Transportation % % Electricity Generation % % Industrial Processes % % Agriculture % % Waste % % LULUCF % % TOTAL % % with coal-fired electricity generation, there is a dramatic drop in overall emissions between the projections. Additional analysis was also conducted to extend the baseline emissions projection to 2050 using long-term drivers such as economic growth, population growth and energy efficiency. Figure 9.1 illustrates a comparison of the BAU emissions projection used for the NDC against the updated emission baseline projection that incorporates new activity data and projection information. By 2050 the original NDC BAU emission projection is 28 MtCO 2e higher than the updated emissions projection. In 2030 this gap is 19.7 MtCO 2e. The updated analysis suggests that instead of requiring 43 MtCO 2e of emission reductions in 2030 to meet the 30% emission reduction target, Kenya may only need 24 MtCO 2e of emission reductions in all sectors from this updated baseline to meet the target (equivalent to 100 MtCO 2e in 2030). One conclusion that could be reached when considering the updated analysis is that the original NDC baseline may have been too conservative and that emissions were not ever likely to grow to 143 MtCO 2e in However, there are a number of very important factors that need to be carefully considered and that justify both the BAU emission projection and 30% emission reduction target. 1. First, the original NDC baseline was consistent with the plans and objectives of the government at that time and it is very difficult to separate out what are emission reductions due to new policy and climate action, and what are emission reductions due to not achieving economic growth or not implementing projects that had been proposed (e.g., energy projects). 2. Second, there continues to be high uncertainty in the projections, especially in regard to the development of emission intensive industries (e.g., oil and gas, chemical industry, steel, aluminum, coal mining). These industries currently don t exist in Kenya and have not been included in the 2050 projection, but there are potential projects, especially in the oil and gas sector. Although not enough detail is currently available to include these potential developments, they could have significant impact. Update of Emission Projections 3

8 Figure 9.1: Comparison of NDC BAU Emissions Projection with November 2016 Updated Emissions Projection Original NDC BAU Emissions Projection GHG Emissions MtCO2e Updated Emissions Projection November 2016 GHG Emissions MtCO2e Waste Industrial Processes Energy Demand Transportation LULUCF Electricity Generation Agriculture For example, even a modest level of natural gas development that would meet 10% of overall demand in 2015 for petroleum products (~ 0.5 billion m 3 per year), would result in increased production and combustion emissions of about 1.2 Mt CO 2e. This is similar as well to the addition of one 200 Megawatt (MW) coal plant operating at a 70% capacity factor. 3. Third, even a small change in long term GDP growth, which is one of the main drivers of emissions can have a significant impact on the projection. A reduction in annual GDP growth of 0.5% per year from 2015 to 2030 results in a difference of more than 3.5 MtCO2e. As short-term and medium-term economic growth projections are updated they will have an impact on the projection. 4. Fourth, the emission projection tries to account for improvements in emission intensity of production and service. For example, an autonomous energy efficiency Update of Emission Projections 4

9 improvement factor is incorporated in the model for different end-uses. While the baseline is not overly aggressive it still means for example that a new passenger car sold in 2030 is more than 30% more efficient than a new car purchased today. Large technological shifts, that have dramatic impact on emission intensity, for example, large scale market penetration of electric vehicles, are not included in the baseline. If for example, electric vehicles were to achieve a market penetration of 20% by 2030, this would reduce overall baseline emissions by as much as 2.5 MtCO 2e. 5. Fifth, the ambitious target of a 30% emission reduction was decided and set against the expected baseline emissions in 2030 of the original BAU emission projection scenario. As such the target reflected the circumstances and the emission levels of the original scenario, and what was deemed feasible and achievable by the Government of Kenya. Based on these factors it is clear that the 30% emission reduction target only applies to the original BAU emissions projection, and that this target represents not exceeding 100 MtCO2e emissions in Kenya in The updated emission projection baseline developed and presented in this chapter is only useful for assessing what needs to be done to meet the NDC emission reduction target and to provide greater clarity of how the overall target could be achieved at the sectoral level. In any circumstance, there are still considerable uncertainties with the projection. Important data gaps and assumptions include: 1. The use of the Vision Expansion Scenario as a realistic baseline for overall electricity generation in Kenya. Note that we do not assume the same penetration of renewables in the baseline as the Vision Expansion Scenario and that we assume that much of this renewable capacity would still be met by cheaper coal generation. 2. Assumption that most fuel demand for cement production will be met by coal. 3. Assumption that the Oil Refinery has not been operating since 2014 and is not expected to restart. 4. Estimates of the harvest and use of wood products (such as fuelwood, charcoal and timber) and the level of carbon stocks in Kenya s forests are based on old data and have considerable levels of uncertainty. New data from on-going initiatives, such as the System for Land-based Emissions Estimation in Kenya (SLEEK) modeling, could potentially significantly reduce these uncertainties. 5. Livestock population estimates are primarily from the last Agricultural Census The projection adopts the BAU GDP growth projection in the Green Economy Strategy and Implementation Plan (GESIP) as reasonable growth projections for making long-term baseline GDP estimates. 6 Significant variations in actual GDP growth or in expectations will lead to significant differences in emissions. 7. The projection currently does not assume any production of oil and gas. A scenario of oil and gas production that includes volumes and dates of planned activities could be included in the projection if it were available. 8. The expected thermal efficiency of Lamu or Kitui coal plants that are planned or under construction is around 40% in the baseline. Significant deviation from this baseline efficiency could alter the emission projection. Update of Emission Projections 5

10 9.3 Setting Sectoral Targets against the Updated Emission Projection Kenya s INDC seeks to abate its overall GHG emissions by 30% by 2030 relative to the BAU scenario. However, this does not necessarily translate into a 30% emission reduction target for each of the individual six mitigation sectors (energy, transportation, agriculture, LULUCF, industrial processes and waste). Significant work conducted both for NCCAP and updated in the SNC examined the technical potential of emission reductions related to the six sectors. This technical potential provided a basis for determining the overall 30% target for Kenya, but each sector had widely differentiated potential as well as costs. The mitigation options identified in the NCCAP work almost exclusively achieve emission reductions within a single sector. These mitigation options are sectoral in nature and would need to be supported and implemented by agencies and ministries responsible for the given sector. As such, sectoral strategies will be key to achieving the overall emission reduction target, and it makes sense to set expectations for mitigation actions and sectoral targets individually for each sector. Rather than a fixed emission reduction target for each sector, a range of emission reductions is proposed. This range provides several advantages including: 1. Flexibility to the responsible ministries and agencies to select from a menu of potential options for the individual sector; 2. The ability to adjust the scale of implementation and hence the emission reductions of any individual mitigation option; 3. The ability to adjust over time to both uncertainties in the baseline emission projections; as well as uncertainties in the potential for actions to reduce emissions. 4. Flexibility for emission reductions to be achieved in the sector where it makes most sense from a political, economic or social standpoint. For example, if emission reductions are lower than expected in one sector, a mitigation option in another sector could be identified quickly to ensure that the overall emission reductions are still achieved. A review of the transport sector can illustrate how sectoral targets might work. In this sector, the NCCAP identified seven potential mitigation options. These mitigation options had an estimated mitigation reduction technical potential of 6.9 MtCO 2e. However, a transport expert group has since been deemed the biodiesel unfeasible. 7 Accounting for only six possible options reduces the technical potential to 5.7 MtCO 2e. Technical potential is of course different from actual potential, and it should be assumed that the technical potential is the limit to what is achievable unless additional mitigation options are considered. The transport sector accounted for approximately 8.0% of the total technical potential of emission reductions from all mitigation options in all sectors, whereas it is expected to account for 14% of total emissions in This suggests that transport will likely have a lower contribution to emission reductions than other sectors. A proportional contribution target for the sector, that reflects the technical mitigation potential of the sector, can be determined by multiplying the fraction of transport technical potential to total technical potential (8.0%) by the required emission reductions in 2030 (24 MtCO 2e in 2030) based on the updated emission baseline projection. This sets a proportional Update of Emission Projections 6

11 contribution target for the sector at 1.94 MtCO 2e. The next step would be to consider what would be a reasonable low and high range around this target. Where a low target could be adopted as a minimum requirement for implementation with a high level of certainty, and a high target could guide responsible ministries and agencies in terms of what they should objectively plan and prepare for should they be required. One possible methodology could be that the floor should be aligned with the proportional contribution target so that Kenya can be reasonably assured that it will achieve the NDC target, even if all sectors only achieve their minimum targets. The ceiling or high range of the target could correspond to the emission reductions that were required under the original BAU scenario to achieve the 30% target, so for example the high target would be 3.46 MtCO 2e for the transport sector (8.0% of 42.9 MtCO 2e). Table 9.3 summarizes possible target ranges and technical potential data for the individual sectors based on the suggested methodology; however, further work needs to be undertaken to update mitigation technical potentials. Table 9.3: Emission Reduction Technical Potential and Contribution to Target in 2030 Sector / Sub-Sector INDC BAU Sector GHG Emissions in 2030 (MTCO 2 eq) Updated Sector GHG Emissions in 2030 (MTCO 2 eq) INDC Technical Potential GHG Emission Reductions (MTCO 2 eq) 1 Low Target Equal Proportional Target GHG Emission Reductions (MTCO 2 eq) High Target Equal Proportional Target GHG Emission Reductions (MTCO 2 eq) Forestry Electricity Generation Energy Other Energy Demand Transportation Sub-Total Agriculture Industrial Processes Waste Total Note: The source of technical potential data is the Government of Kenya (2015), Second National Communication. This data has not been updated and is based on emissions of 143 MtCO 2 e in Update of Emission Projections 7

12 Endnotes 1 Government of Kenya (2015). Kenya s Intended Nationally Determined Contribution, 23 July Nairobi: Ministry of Environment and Natural Resources. Accessed at: pdf 2 Government of Kenya (2013). National Climate Change Action Plan, Nairobi: Ministry of Environment and Natural Resources 3 Government of Kenya (2015). Kenya: Second National Communication to the United Nations Framework Convention on Climate Change. Nairobi: National Environment Management Authority. 4 Government of Kenya (2016). Electricity Generation and Transmission Master Plan Nairobi: Ministry of Energy and Petroleum. 5 Government of Kenya (2011). Updated Least Cost Power Development Plan Study Period: Nairobi: Energy Regulatory Commission. 6 Government of Kenya (2015). Kenya Green Economy Strategy and Implementation Plan (GEISP). Nairobi: Ministry of Environment and Natural Resources. 7 Ministry of Transport and Industrialization, and Ministry of Environment and Natural Resources (2016). Kenya s Intended Nationally Determined Contribution (INDC) - Transport Sector Analysis: Meeting of Transport Sector Expert Group (19 th October). Update of Emission Projections 8