Kenya s Climate Change Action Plan: Mitigation. Chapter 2: Preliminary Greenhouse Gas Inventory. Seton Stiebert (IISD)

Transcription

1 Kenya s Climate Change Action Plan: Mitigation Chapter 2: Preliminary Greenhouse Gas Inventory Seton Stiebert (IISD) August 2012

2 The website for Kenya s Climate Change Action Plan can be accessed at: Kenya s Climate Change Action Plan: Subcomponent 4 Mitigation Chapter 2: Preliminary Greenhouse Gas Inventory Mitigation Team: Deborah Murphy, Seton Stiebert, Dave Sawyer, Jason Dion, Scott McFatridge, International Institute for Sustainable Development Laura Würtenberger, Lachlan Cameron, Raouf Saidi, Energy Research Centre of the Netherlands Peter A. Minang, ASB Partnership for the Tropical Forest Margins at the World Agroforestry Centre Tom Owino, ClimateCare Peterson Olum International Institute for Sustainable Development IISD is a Canadian-based, public policy research institute that specializes in policy research, analysis and information exchange. The institute champions sustainable development through innovation, research and relationships that span the entire world Energy research Centre of the Netherlands ECN develops high-quality knowledge and technology for the transition to sustainable energy management. ECN introduces this knowledge and technology to the market. ECN s focus is on energy conservation, sustainable energy and an efficient and clean use of fossil fuels. ASB Partnership for the Tropical Forest Margins at the World Agroforestry Centre ASB is the only global partnership devoted entirely to research on the tropical forest margins. ASB aims to raise productivity and income of rural households in the humid tropics without increasing deforestation or undermining essential environmental services. For further information, please contact: Deborah Murphy, IISD Tel: dmurphy@iisd.ca Laura Würtenberger, ECN Tel: wuertenberger@ecn.nl This document is an output from a project funded by the UK Department for International Development (DFID) and the Netherlands Directorate-General for International Cooperation (DGIS) for the benefit of developing countries. However, the views expressed and information contained in it are not necessarily those of or endorsed by DFID, DGIS or the entities managing the delivery of the Climate and Development Knowledge Network*, which can accept no responsibility or liability for such views, completeness or accuracy of the information or for any reliance placed on them. 2012, All rights reserved * The Climate and Development Knowledge Network ( CDKN ) is a project funded by the UK Department for International Development (DFID) and the Netherlands Directorate-General for International Cooperation (DGIS) and is led and administered by PricewaterhouseCoopers LLP. Management of the delivery of CDKN is undertaken by PricewaterhouseCoopers LLP, and an alliance of organisations including Fundación Futuro Latinoamericano, INTRAC, LEAD International, the Overseas Development Institute, and SouthSouthNorth.

3 Table of Contents 2.1 Introduction Emissions Reference Case Agriculture Forestry Electricity Generation Energy Demand Transportation Industrial processes Waste Endnotes... 45

4 Abbreviations AFOLU BAU BOD CH 4 CO 2 CO 2e DDOCm FAO FOD GDP GHG Gt GWh ha IPCC kg Ktoe LPG LULC m 3 agriculture, forestry and other land use Business as usual biological oxygen demand methane carbon dioxide carbon dioxide equivalent mass of dissimilable degradable organic carbon Food and Agricultural Organization of the United Nations First Order Decay gross domestic product greenhouse gas gigatonne gigawatt hour hectare Intergovernmental Panel on Climate Change kilogram kilotonne oil equivalent liquefied petroleum gas land use and land-use change cubic metre MPND Ministry of State for Planning, National Development and Vision 2030 MSD medium-speed diesel MT megatonne MW megawatt N 2O nitrous oxide NAMA nationally appropriate mitigation action NOX nitrogen oxide NH 3 ammonia REDD+ reducing emissions from deforestation and forest degradation plus the role of conservation, sustainable management of forests and enhancement of forest carbon stocks SWDS solid waste disposal sites SC4 Subcomponent 4 T21 Threshold 21 TJ terajoule TWh Terawatt hour ULCPDP Updated Least Cost Power Development Plan UNEP United Nations Environment Programme UNFCCC United Nations Framework Convention on Climate Change

5 2.1 Introduction Subcomponent 4 (SC4) Mitigation of Kenya s Climate Change Action Plan analyses lowcarbon development options through a top-down scenario assessment of potential actions in the six mitigation sectors set out in Article 4.1 of the United Nations Framework Convention on Climate Change (UNFCCC): energy, transport, industry, agriculture, forestry and waste management. The holistic, sectoral analysis provides the evidence base for the prioritizing of low-carbon development options and developing proposals for nationally appropriate mitigation actions (NAMAs) and actions to reduce emissions from deforestation and forest degradation plus the role of conservation, sustainable management of forests and enhancement of forest carbon stocks (REDD+). This chapter describes the methodology used to develop the preliminary GHG emissions inventory for 2010 and the reference case projecting emissions out to 2030 for the entire Kenyan economy and by sector. The information is organized by the seven sectors analysed in the low-carbon scenario analysis: energy (electricity generation and energy demand), transportation, industrial processes, agriculture, forestry and waste. This inventory is not suitable for reporting to the UNFCCC, but is a very strong starting point and can easily be built on. Section 2.2 describes and provides context for the overall emissions reference case. Sections 2.3 through 2.9 provide the specific methodologies and data used to generate an emission baseline reference case to 2030 for each of the seven sectors. 2.2 Emissions Reference Case As illustrated in Figure 2.2, the reference case included the development of an inventory of historical emissions from 1990 to 2010, and the projection of annual emissions out to This formed the reference case or the baseline against which to demonstrate the abatement potential of low-carbon development options out to Inventory of historical greenhouse gas emissions An independent GHG emissions inventory and forecast was developed because the last inventory for Kenya was completed for the year 1994 for the first national communication. 1 No comprehensive emissions inventory has been completed since then. Several partial and less rigorous inventories have been developed, including the Threshold 21 (T21) model produced by the Ministry of State for Planning, National Development and Vision 2030 (MPND) and a forecast by the Stockholm Environmental Institute. 2 Kenya s 1994 inventory divided emissions between six major sectors that align with the 1996 Intergovernmental Panel on Climate Change (IPCC) guidelines for conducting emissions inventories. 3 The Revised 2006 IPCC Guidelines divide emissions into four major sectors. 4 The low-carbon analysis contained in this report began with the calculation of historical emissions from 2000 to This detailed preliminary inventory set out GHG emissions in the four major sectors in the 2006 IPCC guidelines. 5 This preliminary GHG inventory used primarily Tier 1 approaches and was informed by a comprehensive review of the literature. Data availability varied by sector, with uncertainties in data much higher in the agriculture and forestry and other land use sectors. The local validation process (see Chapter 1) helped to fill data gaps in the inventory process, identifying potential sources of information and verifying assumptions. Emissions in this preliminary inventory were then allocated across the six mitigation sectors identified in Article 4.1(c) of the UNFCCC, 6 examining the energy sector from demand and 1

6 supply perspectives. The relationship between the six sectors of the low-carbon analysis and the major IPCC sectors in the 2006 and 1996 guidelines is set out in Table 1.1 below. Table 1.1: Relationship of emission baseline reference case sectors to IPCC guideline sectors Low-carbon scenario analysis sectors (from Article 4.1 of the UNFCCC) Energy Demand Electricity Supply Transportation Industrial Processes Agriculture Forestry (and other land use) 2006 IPCC guideline sectors Energy Industrial Processes and Product Use Agriculture, Forestry and other Land Use (AFOLU) 1996 IPCC guideline sectors Energy Industrial Processes Solvent and other Product Use Agriculture Land-Use Change and Forestry Waste Waste Waste The agriculture and forestry and other land use (AFOLU) sectors were the largest emitters, accounting for approximately two-thirds of emissions in 2010, mainly due to emissions from livestock and deforestation,. Energy demand was the next largest emitting sector in 2010, accounting for about 16 percent of emissions, followed by transportation at about 10 percent. Figure 2.1 and Table 2.2 illustrate the specific sources that contributed to the total estimated emissions in The data collected and analyzed to generate the historical emissions could be used to support the development of a national GHG emissions inventory for Kenya for the year 2010 or any other historic year. Figure 2.1: Total emissions by sector in 2010 Forestry 19.6 MT Agriculture 20.5 MT Waste 0.8 MT Industrial Process 2.6 MT Energy Demand 9.8 MT Electricity 2.2 MT Transportation 6.0 MT 2

7 Table 2.2: Historical emissions: 2000, 2005 and 2010 (MtCO2e) Sector Agriculture Forestry Electricity Generation Energy Demand Transportation Industrial Processes Waste Total Projection of emissions to create reference case Historical trends and projections of sector and economic growth then were used to project annual emissions out to 2030, illustrated in Figure 2.2. These projected emissions to 2030 form the reference case that is used as the baseline against which to demonstrate the expected abatement potential in each of the four major IPCC sectors. The emissions are then allocated across the six sectors of the low-carbon scenario assessment by dividing the energy sector into electricity supply, energy demand and transportation; and the AFOLU sector into agriculture and forestry. Figure 2.2: Emissions baseline reference case 120 Emissions Mton CO2- eq Waste Industrial Process Forestry Energy Electricity Transportation Agriculture 3

8 2.3 Agriculture Methodology and data The agriculture sector is combined with the forestry and other land use sectors, the AFOLU sector in the IPCC 2006 Guidelines for developing emission inventories. 7 This report examines the agriculture sector separately from the forestry and other land-use sector to enable an assessment of emissions in the each of the six mitigation sectors set out in Article 4.1(c) of the UNFCCC. Both sectors involve carbon fluxes as a result of the management of lands to some degree, and efforts have been made to avoid double counting by clearly delineating between the sectors. The forestry sector analysis includes the conversion of nonagricultural land-uses to agriculture and the management of plantations. All carbon releases and sinks that are a result of a land conversion from one type to another are included in the forestry sector. The management of soils on agricultural lands, such as cultivation and tillage, are assessed in the agricultural sector as long as they do not involve conversion to a land use other than agriculture. The agricultural sector does not include energy emissions from fuel combustion, which are included within other sectors such as transportation and the household, commercial and industrial energy sectors. The agricultural sector is currently the largest source of GHG emissions of all analysed sectors. More than one-third of total national emissions are from this sector alone. Despite its prevalence, data required to calculate GHG emissions is lacking and considerable uncertainty remains in the calculation of agricultural emissions compared to the energy demand, energy supply, industrial processes and waste sectors. An emissions baseline for the agriculture sector was developed by using a number of Tier 1 approaches from the IPCC 2006 Guidelines. 8 Four different types of emission sources are considered in the analysis: Enteric fermentation and manure management from livestock; Burning of agricultural residues; Nitrogen fertilizer use; and Flooding rice. Calculation methodologies for each emission source are provided below. Specific data and assumptions are provided in the following section. Enteric fermentation and manure management emissions from livestock Emissions from livestock are estimated using Equation 2.1 below. Equation 2.1 Emissions!"! = Population!"#$%&'() Emission Factor!"#$%&'() Emissions CH4 Population Livestock Emission Factor Livestock = methane emissions (kg GHG) = total population of each type of livestock = default emission factors from 2006 IPCC Guidelines that are specific to climate region of Kenya in the livestock in question 4

9 Burning of agricultural residues Emissions from the burning of grazing lands and agricultural residues from croplands are calculated using Equation 2.2 below. Equation 2.2 Emissions!"! = A!"#$ M!"#$ C! C!" EF!"! Emissions Fire = amount of greenhouse gas emissions from fire (kg methane [CH 4] and nitrous oxide [N 2O]) A Type M Type C f = area burnt, hectares (ha) = mass of fuel available for combustion for each area type (tonnes/ ha) = combustion factor, dimensionless EF GHG = emission factor of dry matter burnt, (g/ kg CH 4 and N 2O) Nitrogen fertilizer use Emissions from the application of synthetic nitrogen fertilizers are estimated using Equation 2.3 below. Equation 2.3 Emissions!!! = N!"#$ [ 1 FRAC!"#$ EF!"#$ + 1 FRAC!"#$! EF!"#$! ] Emissions N2O = amount of N 2O emissions from fertilizer use (kg N 2O) N FERT = total use of synthetic fertilizer in country, (kg N/yr) FRAC GASF = fraction of total synthetic fertiliser nitrogen that is emitted as NOX or NH 3 (kg N / kg N) FRAC LEACH EF GASF EF LEACH = fraction of all N added to/mineralised in managed soils in regions where leaching/runoff occurs that is lost through leaching and runoff, (kg N / kg N). = default emission factor from atmospheric deposition, (kg N 2O / kg nitrogen emitted) = default emission factor from N leaching and runoff, (kg N 2O / kg nitrogen emitted) Flooding Rice Methane emissions are the result of anaerobic decomposition of organic material in flooded rice field production and are estimated using Equation 2.4 below. Equation 2.4 Emissions!"! = A EF!"! 5

10 Emissions CH4 = amount of methane emissions from rice cultivation (kg CH 4) A Type EF CH4 = area under cultivation, ha = emission factor per hectare based on results from the first National Communication, (4.2x10-9 kg CH 4 / ha under cultivation) Data and Assumptions Enteric fermentation and manure management emissions from livestock Historic livestock populations were obtained from the Ministry of Livestock and are summarized in Table Table 2.2: Historic livestock population (head of livestock) Cattle 15,613,871 17,698,699 16,879,358 17,537,332 18,383,183 17,468,000 17,862,852 Sheep 13,587,784 17,354,889 14,315,764 16,308,120 17,135,919 17,129,000 17,562,105 Goats 19,842,110 27,760,597 20,417,414 27,927,369 28,951,799 27,740,000 28,174,158 Pigs 326, , , , , , ,413 Rabbits 423, , , , , Asses 416, , , , ,804 1,832,519.. Camels 824, ,300 1,058,300 1,006,300 1,132, ,200.. Chickens 32,083,184 31,920,006 30,933,474 30,625,384 32,986,512 31,827,000 30,398,033 Default emission factors to calculate emissions were taken from the 2006 IPCC guidelines 10 and are based on regional defaults for Africa and developing countries and are provided in Table 2.3 below. Table 2.3: Emission factors for different types of livestock Type of Livestock Emission Factor Enteric Fermentation CH 4/head/yr Emission Factor Manure Management CH 4/head/yr Dairy Cattle 40 1 Cattle 31 1 Sheep Goats Pigs 1 1 Rabbits 0 0 6

11 Type of Livestock Emission Factor Enteric Fermentation CH 4/head/yr Emission Factor Manure Management CH 4/head/yr Asses Camels Chickens Future growth rates of livestock between 2010 and 2030 are likely to be below average historic growth rates of livestock as there is mounting evidence that current populations of livestock on rangelands in Kenya may be near or above their carrying capacity. 11 Historic growth rates of livestock between 2000 and 2010 for the main grazing land livestock - cattle, sheep, goats and camels - were 1.4, 2.6, 3.6 and 1.6 percent respectively. Almost no growth was observed for chickens while the growth in asses was observed to be 17.9 percent over the same time period. There is considerable uncertainty in the livestock census data, but the overall trend is increasing. Growth in agricultural GDP is forecast to be 3.0 percent in Vision 2030, but it was assumed that future livestock growth would be substantially below this growth level. All livestock species with the exception of chickens and pigs were forecast to grow at an annual rate of 1.3 percent per year between 2011 and Chickens and pigs were forecast to grow at a rate of 3 percent per year as these species tend to be under more intensive agricultural production and therefore are not subject to the same carrying capacity constraints as rangeland livestock. Burning of agricultural residues The area of land where agricultural residues are burned was estimated based on information provided by the Ministry of Agriculture on the percentage of different crop and land areas that are burned. 12 Table 2.4 identifies the total area of the type of crop or land were agricultural residues were burned. Table 2.4: Area of grazing land and croplands burned in 2010 (hectares) Type of Crop or Land 2010 (ha) Grazing Lands 436,000 Maize 2,008,346 Wheat 160,043 Sugarcane 157,583 Rice 20,181 The amount of biomass residue burned related to each crop area and the emission factors for non-carbon related emissions (CH 4 and N 2O) were was based on 2006 IPCC default values and are provided in Table 2.5 below. 13 7

12 Table 2.5: Quantity of agricultural residue and emission factors for burning of grazing land and croplands Type of Crop or Land Residue Burned (t/ha) Emission Factor (kgco 2e/ t burned) Grazing Lands Maize Wheat Sugarcane Rice Future growth in the amount of agricultural residues burned in the baseline is estimated based on a 3 percent annual growth in the production of these crops which is consistent with historic growth in the sector between 2000 and Nitrogen fertilizer use The amount of synthetic fertilizer used in Kenya was estimated from data published by the FAO. Table 2.6 provides the historic quantity of synthetic nitrogen fertilizers applied in Kenya between 2002 and Table 2.6: Amount of different types of nitrogen fertilizer applied in Kenya (2002 to 2009) Nitrogen Fertilizer Ammonium nitrate ,746 1,006 1,207 1,207 Ammonium sulphate 3, , ,029 1,340 1,514 1,514 Calcium ammonium nitrate 0 59,801 30,700 51,456 59,739 69,714 78,080 78,080 Urea 49,866 24,288 45,084 25,017 41,071 28,554 29,982 29,982 Urea and ammonium nitrate solutions Other nitrogen & phosphates compounds Other nitrogen & phosphorus compounds ,715 29,393 25,350 21,082 18,855 20,486 17,793 3, ,949 NPK complex <=10kg 0 64,083 47,168 76,375 60,624 70,325 16,589 0 NPK complex >10kg 135,845 12,656 16,985 21,948 26,980 18,834 20,717 89,516 Potassium nitrate 2, , , ,558 8

13 The percentage of nitrogen by mass in different fertilizers was estimated based on the chemical composition of the fertilizers and is provided in Table 2.7 below. The values Table 2.6 in are multiplied by these percentages to determine the total synthetic nitrogen that is applied in Kenya to agricultural lands. Table 2.7: Estimated nitrogen content of different types of nitrogen fertilizer Nitrogen Fertilizer Percent Nitrogen Ammonium nitrate 35% Ammonium sulphate 21% Calcium ammonium nitrate 25% Urea 47% Urea and ammonium nitrate solutions 47% Other nitrogen & phosphates compounds 18% Other nitrogen & phosphorus compounds 18% NPK complex <=10kg 18% NPK complex >10kg 18% Potassium nitrate 14% Default fractions of the total synthetic fertilizer nitrogen emitted to the atmosphere or leached and default emission factors are from the 2006 IPCC Guidelines and summarized in Table 2.8 below. Table 2.8: Default fractions and emission factors for releases from the application of synthetic fertilizers Default Emission Factors and Fractions Value FRAC GASF 0.1 FRAC LEACH 0.3 EF GASF 0.01 EF LEACH Flooding Rice The area of historic rice cultivation was taken from the Food and Agriculture Organization s (FAO) database 15 and is provided in Table 2.9 below. 9

14 Table 2.9: Area of rice under cultivation between 2001 and 2010 (hectares) Metric Area (ha) 13,200 13,000 10,781 13,322 15,940 23,106 16,457 16,734 21,829 20, Data availability and uncertainty The agricultural sector is the largest source of GHG emissions of the seven sectors considered in this low-carbon sectoral study. Despite the size and prevalence of the sector, data required to calculate GHG emissions is lacking and considerable uncertainty remains in the calculation of emissions when compared to the energy demand, energy supply, industrial processes and waste sectors. Livestock emissions account for approximately 30 percent of total emissions in Kenya, yet it is necessary to use default emission factors that are not country specific to estimate these emissions. The uncertainty of these emission factors is reported to be in the range of ± 30 percent to 50 percent. 16 The uncertainty in the forecast baseline emissions is even greater as estimates of future populations of livestock also have considerable uncertainty. Decreasing the annual growth rate of all livestock by even a small amount from 1.3 percent to 1.0 percent would reduce overall agricultural emissions in 2030 by 5 percent (1.4 megatonnes [Mt]). The burning of agricultural residues on grazing lands and crop lands also has considerable uncertainty as there are only poor estimates of the total areas of these lands where this practice occurs. Increasing the assumed area burned by 20 percent would result in overall agricultural emissions in 2030 increasing by one percent (0.4 Mt). Uncertainty related to other emission sources including rice flooding and nitrogen fertilizer use is also high, but because of the small magnitude of these emissions even an increase of 100 percent in these emissions would increase total agricultural emission in 2030 by less than one percent. 2.4 Forestry Methodology The emissions baseline was developed using PATH modelling software. PATH is a state-andtransition model that accounts for land use/land cover (LULC) change. There are no built-in relationships in PATH. It is designed to be flexible, permitting the input of any land states and transition types that the user defines. This is an asset because it allows the modeller to work meaningfully with limited data sets. It can be used spatially, so that each point on a map corresponds to a given land type; or non-spatially, where the land and its distribution among types remain in the abstract. This abstract type of specification is useful in contexts (such as Kenya) where the specific type of each unit of land as represented on a map is not known with a high degree of accuracy. The Kenyan PATH model uses 500,000 land units corresponding to area parcels of around 12 hectares. The model tracks the age of each state since its last transition. In the case of forests, this age represents the relative amount of above ground and below ground biomass that has accumulated. An age zero forest for example would have all above ground biomass removed, where-as an age twenty year forest would have an accumulate biomass over twenty years as represented by specific biomass curves for different forest types. PATH tracks the state that each unit of land is in and its age. However, by also tracking in the model the amount of carbon held in a unit of a given land type, it is possible for it to model 10

15 the amount of carbon being held in different land classes and the total carbon stock. Changes in this stock can then be used to determine the carbon emissions resulting from LULC change. For each land state it is possible to define and track separately above-ground carbon, below-ground carbon, soil carbon, and total carbon stocks. The Kenya customization of the PATH model relies on FAO data 17 as well as various GoK sources outlined in the data and assumptions section below to define the following land types and the distribution of land between them: Bushland; Farms without trees; Farms with trees; Forest; Grassland; Mangrove; Public plantation; Private plantation; Woodland; and Settlements. Each of these land types (other than grasslands, farms without trees and settlements) has within it four possible classes: early, mid and late, and degraded. Early, mid, and late classes describe the age of the land, and have different carbon stocks associated with each. As the lands age, they move deterministically from early to mid at 21 years, and from mid to late at 121 years, with their associated carbon stock growing at each step. Degraded lands also track the carbon stock of the land, but degraded land does not graduate to older age classes, since degraded land is modelled to capture land that is not able to recover. In addition to these four sub-classes, protected versus unprotected versions of most of these land types are also tracked. Deterministic age transitions are the simplest type of state class transition in the model. However, a range of other transition types drawn from Forestry Master Plan and the FAO study are also modelled to capture the various drivers and trends around LULC that are found in Kenya. 18 Below is a partial view of a visual representation of the different states (squares) and transitions between them (arrows) that exist. The transitions in the Kenyan customization of PATH include: LULC: Bushland -> Farm LULC: Forest -> Farm LULC: Grassland -> Farm LULC: Mangrove -> Farm LULC: Plant Public -> Farm LULC: Settlement -> Farm LULC: Woodland -> Farm LULC: Plant Public -> Plant Private LULC: Farms without Trees -> Farms with Trees LULC: Farms without Trees -> Plant Private Wildfire Wood removal Figure 2.3 below illustrates all of the specific states and transitions that are included in PATH model. 11

16 Figure 2.3: States and transitions that are included in the Kenya PATH Model A sizable quantity of land is transitioned every year from various other land states to farms (as identified by blue arrows pointing towards farm No Tree and Farm Tree states in the diagram). These transitions are modelled by setting the transition to and from states, and then elsewhere setting the amount of land that is transitioned each year from a particular land type to farms, with these transition amounts drawn from forecasted LULC trends from the literature. Wood removal is similarly modelled, where amounts of wood removal are based on harvest volume estimates drawn from the literature. Wood removal transitions move a given land type to an earlier age class, indicating that the land has been effectively made younger and therefore has less above ground biomass stock; however, a portion of this land also becomes degraded, to capture the fact that not all wood harvest is done in a manner where forests can recover naturally. Other transitions are modelled probabilistically, based on their historical likelihood. This is done so that with each run of the model there is an element of uncertainty around drivers that have an inherent element of randomness, such as wildfire. Doing so allows the model to mirror the uncertainty that would exist in the real world around such trends and drivers. However, by doing Monte Carlo analysis, where there are a number of runs of the same model taken together and averaged, any random variations are smoothed out so that the uncertainties incorporated by probabilistic transitions do not disproportionately affect results. The calculation of annual changes in emissions is consistent with IPCC 2006 Guideline methodologies. 19 This method considers the difference between biomass stocks, for a given land use area and two points in time. Once all states and transitions were defined in the model, several parameters were calibrated so that they matched with existing data on LULC. The model therefore begins in 1990, the first year for which there is reliable data, and is run to 2030, the end of the time period considered in the low-carbon scenario analysis. The results from the years are checked against FAO projections for these years, with the aim of making the model correctly express the LULC and carbon emission trends that were seen in this period. Once the model is successfully calibrated to this data it is possible to have confidence in its projections, since the model has now been shown to correctly express by proxy the trends and drivers actually seen in Kenya. 12

17 2.4.2 Data and assumptions In this section, the extent and trend of land-use areas is outlined first, then individual carbon pools for above ground biomass, below ground biomass and soil carbon are described. Finally, PATH model results for carbon are compared to the literature. Land-Use Categories The land-use types and historic areas used in the analysis are identified in Table These land-use areas are identical to the data used in the FAO country study. 20 Table 2.10: Areas of different land-use categories between 1990 and 2010 Land-Use Categories Area (000 hectares) Change in Area (000 hectares) from 1990 to 2010 Indigenous Closed Canopy 1,240 1,190 1,165 1, Indigenous Mangroves Open Woodlands 2,150 2,100 2, Plantation Forests Public Plantation Forests Private Bush-land 24,800 24,635 24,570 24, Grasslands Other Land 6,438 6,291 6,210 6, Grasslands Other Wooded Land 3,863 4,194 4,140 4, Settlements 8,256 8,192 8,152 8, Farms with Trees 1 9,420 10,020 10,320 10, Inland Water Bodies 1,123 1,123 1,123 1,123 - Total Area for Country 58,037 58,037 58,037 58,037 - Note: 1 Figures for the total area of Farmland without trees could not be obtained, because the Forest Resource Assessment 2010 does not provide one, and it is unclear to what extent Farms with Trees corresponds to the different FAOStat agricultural land-use categories. Kenya s new 2005 Forestry Act seeks to stop excisions of state forests and the degazetting of forests to allow them to be used for agriculture and settlements. Excisions between 1993 and 2002 are reported as 11,783 hectares per year, 21 which is similar to the historic trend of lost forest area between 1990 and 2010 that is identified in the FAO data above. Stopping excisions would reduce deforestation; however, there are no statistics available to identify to what extent excisions or illegal encroachment of forests is still occurring in Kenya. With demand for fuelwood and charcoal expected to continue to increase in the near future it is clear that stopping and slowing down deforestation and the conversion of forests to other land-uses will require substantial efforts to enforce new regulations. Without additional 13

18 information on the status of deforestation, projections of future land-use areas from 2010 to 2030 are based on projections to 2020 available from the Kenya Forestry Master Plan, as well as the FAO s extrapolations of historic trends. These trends are shown in Table 2.11 for each five-year period between 2010 and 2030, based on data from the FAO Forest Resource Assessment. Table 2.11: Projected areas of land-use categories (2010 to 2030) Land-Use Categories Area (000 hectares) Indigenous Closed Canopy 1,140 1,115 1,090 1,066 1,043 Indigenous Mangroves Open Woodlands ,025 2,001 1,977 1,954 Plantation Forests Public Plantation Forests Private Bush-land 24,510 24,448 24,386 24,324 24,262 Grasslands Other Land 6,210 6,170 6,130 6,090 6,051 Grasslands Other Wooded Land 4,140 4,113 4,087 4,060 4,034 Settlements 8,202 8,237 8,248 8,253 8,258 Farms with Trees 10,385 10,533 10,703 10,875 11,044 Inland Water Bodies 1,123 1,123 1,123 1,123 1,123 Total Area for Country 58,037 58,037 58,037 58,037 58,037 Above ground and below ground biomass accumulation for an area of forest typically follows a distinctive declining exponential curve relative to the age of the forest. Measurements indicate that aboveground biomass accumulation is highest when forests are of a young age and over time this biomass accumulation slows down until eventually it nears steady state. Figure 2.4, taken from the Kenya Forestry Master Plan provides an example of a biomass curve for a tropical forest in eastern Africa

19 Figure 2.4: Sample of a biomass curve for a tropical forest in Eastern Africa The PATH model simulates the accumulation of above ground biomass of different land-use categories by estimating growth based on several discrete age ranges. The starting point for these simplified biomass curve definitions are IPCC s default values from the 2006 IPCC Guidelines that identify average above ground biomass growth rates for different types of forests younger than 20 years and older than 20 years for relevant climate domains and ecological zones. The selected biomass intensities for different age classes and land-use categories are identified in Table 2.12 below. Table 2.12: Biomass intensities for different undisturbed land-use categories based on age Land-Use Category Above Ground Biomass Intensity (t biomass / ha) Based on Age of Land-Use Category (years) 0 to to 120 >120 Forest Mangrove Woodland and Bushland Plantation Based on the biomass growth intensities identified in Table 2.12, simplified curves of the above-ground carbon growth rate are expressed in Figure 2.5 for different forest land-use areas, assuming that biomass growth intensities remain constant within each age range. The conversion of biomass to carbon is based on a carbon fraction IPCC default value of 0.47 tonne carbon / tonne dry matter. 15

20 Figure 2.5: Above Ground Carbon Intensities as a function of age for different forest types 250 t C / ha Years Undisturbed Forest Mangrove Woodland Bushland Plant Public Plant Private The model takes account of trees on farm land and grasslands by assuming that the proportion of the total land area covered in trees on farms has the same biomass intensity as forests and the proportion of the total land area covered in trees on grasslands has the same biomass intensity as bushlands. Above ground biomass and carbon intensities for non-forested grassland and farmland do not change over time as these areas are assumed to have achieved a steady state equilibrium where new biomass growth is balanced by removals. IPCC 2006 guideline defaults for the above ground biomass content of 9.28 t/ha for grasslands and 25 t/ha for farmland are applied. 23 Below-ground biomass The PATH model assumes that below ground biomass stocks are directly related to above ground biomass stocks using a shoot to root ratio of 0.24 for all forest vegetation and 0.14 for grasslands. 24 This means that below ground biomass for forests is exactly 24% of the calculated above ground biomass for each land-use type. Soil carbon A detailed soil carbon map is also available from a UNEP-WCMC study 25 and the average soil carbon content of different land-uses is also reported in a UNEP study. 26 However, soil carbon values identified are not easily mapped to the land-use categories used in the model and identified in Table The PATH model employs the following soil carbon intensities indicated in Table 2.13 based on the information from these documents. 16

21 Table 2.13: Average soil carbon content of land-use categories Land-Use Categories Forests (Indigenous Closed Canopy, Indigenous Mangroves, Open Woodlands, Plantation Forests Public & Private) Soil Carbon (t/ha) 80 Bushland 62.7 Grasslands 45.4 Farms with Trees, Settlements 45.4 Inland Water Bodies 0 Wood harvesting Kenyan forests provide an abundance of biomass for fuelwood, charcoal, as well as wood and paper products. Total demand for wood to meet these requirements is based on demand estimates from two main sources. The demand for fuelwood and charcoal is based on data published in an Integrated Assessment of Energy Policy in Kenya. 27 The demand for other products and their growth rate over time are based on data published in the Kenyan Forest Outlook study. 28 Table 2.14 provides a summary of estimated historic and future projected wood removals on a dry ton basis. Table 2.14: Estimates of wood removal from 2000 to 2030 (tons of dry matter) Wood Removal Fuelwood 11,849,207 13,110,755 13,987,949 12,985,023 12,054,007 11,189,744 10,387,448 Charcoal Production 8,197,698 9,070,481 9,642,821 8,792,862 8,017,822 7,311,098 6,666,667 Poles and Posts 840, ,756 1,064,827 1,198,888 1,349,828 1,519,771 1,711,110 Wood Products 194, , , , , , ,310 Paper and Paper Board 111, , , , , , ,998 Forest fires The area of forests that are impacted by forest fires is highly variable from year to year. The model uses a probability distribution around a central average of approximately ha which is based on the historical average area of forest fires between 1980 and This corresponds to an average risk of forest fire for any given area of once every 370 years. Climate change may impact the long-term risk of forest fires; however, there was insufficient data to include this impact. Comparison with other models and methods UNEP has estimated that the carbon in Kenya s biomass and soil amounts to a total of nearly 5 gigaton (Gt), comprised of 0.9 Gt of carbon in above and below ground biomass and about 4 Gt in soils to 1 metre depth. 29 These estimates are similar to the PATH modelling results identified in Figure 2.6, where in 2010 the total carbon in above and below ground biomass 17

22 is 0.95 Gt and soil carbon is 3.2 Gt. Given that the models are based on very different methods and data sets this correlation is not unreasonable. Figure 2.6: PATH model estimates of total carbon in Kenya from 2000 to 2030 (MT C) 4,500 4,000 3,500 MTCO2e Carbon Stock 3,000 2,500 2,000 1,500 1, Soil C Below- ground C Above- ground C Data availability and uncertainty Greenhouse gas emission trends in the forestry sector are hard to determine because of the difficulty in accurately measuring biomass carbon pools for the entire country. Conducting forest and land-use inventories of carbon stocks is complex and the resources required to conduct a detailed forestry inventory is beyond the scope of this project. The Ministry of Forestry and Wildlife is coordinating an multi-sectoral effort to enable more accurate carbon stocks measurements in Kenya using remote sensing and ground-based inventories; however, this information will not be available until The Kenya Forest Service (KFS) is engaged in this multi-year forest mapping project with the knowledge that current estimates are not reliable and have a high degree of uncertainty. The best available data to estimate carbon stocks has been published in the Kenya Forestry Master Plan; however, this information is to a large extent out of date. 30 The most recent estimate available on carbon trends in Kenya is the FAO s Global Forest Resource Assessment Country Report: Kenya. 31 In this study, data and projections from the Kenya Forestry Master Plan are used and extrapolated to estimate carbon sinks and releases of forests in Kenya over time, although changes in carbon stocks in non-forested areas are not included in the estimates. The emissions baseline is based on aggregated estimates of the carbon intensity (tonnes of carbon per hectare) of different land-use categories over time that is a product of a limited number of measurements that have not been updated in more than a decade. While no information is available to estimate the range of uncertainty, the high variability of the abatement estimates cited in the literature indicates that it is certainly higher than most other sectors (for example, electricity, transportation, industrial processes). Small changes in model input values for the emissions baseline can lead to drastically different results. For example, if the above ground biomass of the Farms with Trees land-use 18

23 category increases by 20 percent from 16 m 3 /hectare to 19 m 3 /hectare between 2010 and 2030, rather than remaining constant, total emissions in 2030 would decline 36 percent (3.9 MT). Uncertainty would be greatly reduced by using consistent spatially explicit observations of land use and land-use change using remote sensing and geographic information systems (GIS), which KFS is currently using in its forest mapping project. Variations of carbon intensity per hectare for different climatic regions would also improve the analysis. The PATH model assumes that for any transition between land uses that the difference in average soil carbon is released or absorbed in the same year that the transition occurs. In reality, changes in soil carbon pools usually occur over much longer time periods. As a result, significant changes in the area of land that is transitioned from one land use to another over time could result in an over or under estimation of the soil carbon stock. In addition, the PATH model does not look at changes in soil carbon within specified land-use categories as there is insufficient information available to estimate losses or gains in soil carbon for these land-use categories. More research in Kenya is required to improve estimates in soil carbon stocks. The PATH model also does not account for carbon stored in above ground litter. In relative terms this carbon pool is not very large and FAO data suggests that above ground litter accounts for less than 3 percent of total carbon stocks. Overall, however, the PATH model's emissions baseline do not diverge significantly from what has been found in the literature, especially when one accounts for the numerous elements of uncertainty that exist in these competing estimates. The PATH model's baseline is therefore a fair representation of the LULC related emissions in Kenya, and serves as a useful, rigorously defined starting point for the subsequent analysis of abatement options. 2.5 Electricity Generation Methodology Developing a reference case for Kenya s electricity sector is challenging because there is considerable uncertainty regarding how the sector may grow to meet a large suppressed demand for electricity. Specific plans are in place but they assume very high growth rates. The Updated Least Cost Power Development Plan presumes a 14% per annum growth rate in electricity supply between 2010 and 2030 in the reference case. 32 This compares to historical growth in electricity supply of about 7% between 2000 and The cost to achieve this dramatic growth is estimated at U.S$ 41.4 billion (excluding committed projects) while generation technologies that are expected to make up the vast majority of new supply still face considerable barriers to implementation. 33 Technologies such as nuclear, coal and wind have either not been proven in Kenya or have limited current penetration. Geothermal, which is expected to comprise the largest portion of generation, has high initial costs and risk of resource exploration that must be overcome. An emissions baseline for the electricity sector is developed by estimating the total fossil fuel consumption of different generation technologies and then multiplying the total consumption by appropriate emission factors. This method is the same as the Tier 1 approach used in the 2006 IPCC Guidelines 34 for stationary combustion sources and employs the following equation: Equation 2.5: GHG Emissions from the Electricity Sector Emissions!"!,!"#$ = Fuel Consumption!"#$ Emission Factor!"!,!"#!,!"#!!"#! 19

24 Emissions GHG,fuel Fuel Consumption fuel Emission Factor GHG,fuel = emissions of a given GHG by type of fuel (kg GHG) = amount of fuel combusted (terajoule [TJ]). Fuel Consumption for the electricity sector is estimated by multiplying the total generation (gigawatt hour [GWh]) by the average conversion efficiency of the technology (%) by the conversion factor 3.6 GWh/TJ. = default emission factor of a given GHG by type of fuel (kg gas/tj) Total generation by technology type is estimated by multiplying the installed capacity (megawatt [MW]) of each technology by an average capacity factor (hours per year). The data, assumptions and source references used to estimate both historical and forecast emissions are outlined in the following section. The information for the most part taken from Updated Least Cost Power Development Plan 35, personal communications with the Ministry 36 that update projections of the Medium Term Plan 37 and the 2006 IPCC Guidelines, but expert judgement provided during stakeholder consultations and meetings was also used Data and assumptions The baseline outlined in this section is not identical to the reference case in the Updated Least Cost Power Development Plan. 38 This is because the objective of the baseline emissions forecast is to consider a scenario based on existing policies and regulations and assume no growth in international aid and related international investments. Specifically, any additional international support and investment for electricity generation projects that may be tied to low-carbon development are not included in the emissions reference case, unless the international support and related investments have already been committed. Including such international support would mean that the substantial renewable generation investment in geothermal, wind and small hydro in the aspiration of Vision 2030 reference case could not be part of a NAMA. Consequently, including this potential investment in the baseline would mean that billions of dollars in investment opportunity through NAMA project development could not be considered in the mitigation scenario. Figure 2.7 compares the total installed capacity between 2010 and 2030 presented in the reference case adopted in this low-carbon analysis to the reference case in the Updated Least Cost Power Development Plan. 39 Figure 2.7: Comparison of installed capacity between 2010 and ,000 Installed Capacity (MW) 18,000 16,000 14,000 12,000 10,000 8,000 6,000 4,000 2, ULCDP Reference Case Reference Case 20

25 Revised projections of short-term generating capacity installations shown in Table 2.15 were provided by the Ministry of Environment. 40 Table 2.15 outlines the new generation capacity that is included in the baseline for the years between 2012 and Table 2.15: Information provided from the Ministry of Energy on new additions to capacity New power plants and generating capacity that will come online in the next five years New Additions to Capacity Between 2012 and HYDRO 21MW Sang Geothermal 2.3 MW Eburru Geothermal 75 MW - Olkaria Wind 60MW - Aeolus MSD 81MW - Triumph MSD 84MW - Gulf MSD 87MW - Melec MSD 80MW Muhoroni Wind 6.8 MW - Ngong Geothermal 140 MW Olk4 Wind 300 MW - Turkana Geothermal 36 MW - Olk3 Wind 50 MW - Osiwo Hydro 32 MW- Kindaruma Geothermal 140 MW - Olk 1 4&5 Geothermal 280MW Hydro 6 MW small hydro IMPORT 400MW Coal 600MW - Mombasa Geothermal 140MW Geothermal 45MW For years beyond 2017, the reference case deviates from the Updated Least Cost Power Development Plan 2011 reference case in order to reflect a baseline that is based on existing policies and regulations and assumes no growth in international aid and related international investments, specifically assuming no additional international support that would be tied to NAMAs. Since there is no gas generation in the medium term, the analysis adopts the trend identified in the Updated Least Cost Power Development Plan. Imports also follow the same trend as the ULCDP. All other technologies including coal, mediumspeed diesel (MSD), wind, hydro and geothermal are based on the same rate of growth generation capacity that is installed in the medium term plan (2012 to 2017). In the reference case it is assumed that no nuclear energy will be added before Figure 2.8 compares the installed capacity of different technologies in 2030 in the reference case adopted in the low-carbon analysis to the reference case that is presented in the Updated Least Cost Power Development Plan

26 Figure 2.8: Comparison of installed capacity in 2030 (MW) 6,000 Capacity in 2030 (MW) 5,000 4,000 3,000 2,000 1,000 0 Reference Case ULCDP Reference Therefore total generation capacity under the GHG emissions reference case is 11,287 MW in 2030 versus from 17,220 MW under the Updated Least Cost Power Development Plan. This represents an annual average growth rate in capacity of 11% versus 14% in the Updated Least Cost Power Development Plan. The growth rate assumed in the low-carbon scenario reference case is still considerably larger than historic growth in the economy and the sector. Table 2.16 identifies the total installed capacities of various generation technologies. Table 2.16: Baseline generation capacity by technology type (MW) Generation Type Hydro ,003 MSD ,085 1,415 1,745 Gas Turbine ,414 2,314 Cogeneration Geothermal ,312 1,872 2,734 Wind ,211 Coal 0 0 1,080 1,680 2,280 Nuclear TOTAL 1,486 2,960 5,687 8,246 11,287 Future electricity generation by technology type was estimated by multiplying the installed capacity (MW) of each technology by an average capacity factor for that technology (hours per year). These utilization rates, shown in Table 2.17, are based on those presented in the Updated Least Cost Power Development Plan and where applicable, are an average of peaking and baseline load. 22