National Performance and Benefit Measurement Framework. Section B: Selecting and Monitoring Adaptation Indicators

Transcription

1 National Performance and Benefit Measurement Framework Section B: Selecting and Monitoring Adaptation Indicators November 2012

2 2012 Ministry of Environment and Mineral Resources, Kenya Reproduction of this publication for educational or non-commercial purposes is authorized without prior written permission from the copyright holders provided the source is fully acknowledged. Reproduction of this publication for resale or other commercial purposes is strictly prohibited without prior written permission of the copyright holder. Disclaimer The views expressed in this publication are not necessarily those of the agencies cooperating in the National Climate Change Action Plan process. The designations employed and the presentations do not imply the expression of any opinion whatsoever on the part of the Government of Kenya or cooperating agencies. Mention of a commercial company or product in this publication does not imply endorsement by the Government of Kenya. The use of information from this publication concerning proprietary products for publicity or advertising is not permitted.

3 Contents 1. Executive Summary Introduction Building blocks Monitoring and evaluation policy context Definition of terms Structure of this report Methodology used for indicator selection Top-down indicator development Bottom-up indicator development Indicator definition Performance and benefits Attribution of outcomes to actions Proposed indicators Top-down Institutional Capacity Indicators Bottom-up Vulnerability Indicators Sub-sector coverage Monitoring and Evaluation Framework Current institutional frameworks for M&E Proposed institutional frameworks for M&E Proposed institutional roles in an adaptation M&E framework At the top of the information chain At the bottom of the information chain Linking the bottom of the information chain to the top Extending the information chain to the counties Implications of the new arrangements Data sources Data storage Conclusions Recommendations Annex 1: Definition of Terms Annex 2: Top-down Institutional Adaptive Capacity Indicators National Level... 41

4 Annex 3: Top-down Institutional Adaptive Capacity Indicators County Level Annex 4: Bottom-up Vulnerability Indicators County Level53 Annex 5: Bottom-up Vulnerability Indicators National Level Annex 6: Indicator Data Sheets... 62

5 Abbreviations ASALs Arid and Semi-Arid Lands ATAR Adaptation Technical Analysis Report CAMER County Annual Monitoring and Evaluation Report CBOs Community Based Organizations CCD Climate Change Directorate CCS Climate Change Secretariat CCU Climate Change Unit COMECs County Monitoring and Evaluation Committees CPPMUs Central Planning and Projects Monitoring Units CSF County Stakeholders Forum CSOs Civil Society Organisations DFID Department for International Development (United Kingdom) DMECs District Monitoring and Evaluation Committees DRSRS Department of Resource Surveys and Remote Sensing E-ProMIS Electronic Projects Monitoring System ICT Information and Communication Technology IIED International Institute for Environment and Development KARI Kenya Agricultural Research Institute KCCAP Kenyan Climate Change Action Plan KEIN Kenya Environmental Information Network KFS Kenyan Forest Service KMD Kenyan Meteorological Department KNBS Kenyan National Bureau of Statistics GoK Government of Kenya M&E Monitoring and Evaluation MAMERs Ministerial Annual Monitoring and Evaluation Reports MDAs Ministries, Departments and Agencies MDG Millennium Development Goal MED Monitoring and Evaluation Directorate MEMR Ministry of Environment and Mineral Resources MMECs Ministerial Monitoring and Evaluation Committees MPND Ministry of State for Planning, National Development and Vision 2030 MTP Medium Term Plan NAP National Adaptation Plan NCCRS National Climate Change Response Strategy

6 NDMA NEMA NGO NIMES NPBMF NSF SC TAMD TWG WRMA National Drought Management Authority National Environment Management Authority Non-Governmental Organisation National Integrated Monitoring and Evaluation System National Performance and Benefit Measurement Framework National Stakeholders Forum Sub component Tracking Adaptation and Measuring Change Thematic Working Group Water Resources Management Authority

7 Acknowledgments The SC6 team are indebted to a large number of individuals and institutions with whom the team have consulted as part of this work. Organisations were consulted via the county consultations, via meetings in Nairobi and via the SC6 Stage 1 and Stage 2 workshops. In addition, the expertise and insights provided by the NPBMF Team s Thematic Working Group and the Climate Change Secretariat has been invaluable in guiding the work. The SC6 team are Land, Trees and Sustainability (LTS) Africa Ltd, Ricardo-AEA and Baastel. The lead authors for this SC6 document are John Mayhew and Irene Karani from LTS Africa, with input from other SC6 team members.

8 1. Executive Summary This report covers the design of the adaptation components of the National Performance and Benefits Measurement Framework (NPBMF), focusing on the development of adaptation indicators and an accompanying monitoring and evaluation (M&E) framework that sits within the Government of Kenya. The methodology used for the identification of adaptation indicators is known as Tracking Adaptation and Measuring Development (TAMD). It has been developed by the International Institute for Environment and Development (IIED) and has been trialled on projects in a number of countries with some success. The TAMD methodology describes the development of indicators that reflect institutional adaptive capacity (measuring top-down adaptations) and vulnerability (measuring bottom-up adaptations), rather than climate impacts or risk. By doing this, actions that focus on the development end of the adaptation continuum are measured, rather than costly technological fixes that may have limited developmental benefits. Top-down institutional adaptive capacity indicators Given that there are over 300 adaptation actions proposed in the National Adaptation Technical Analysis (ATAR), it was inevitable that a large number of indicators would be required. In total, 62 national level, process-based indicators measuring institutional adaptive capacity were identified. The list of actions in the ATAR will be significantly reduced prior to the production of the NAP. Therefore, the number of indicators actually required could be significantly less than the 62 identified here. Based on these indicators, a total of 28 county level, outcome-based indicators were identified, subsequently reduced to a shortlist of 10. The idea is that these indicators measure the effectiveness of national initiatives to build institutional adaptive capacity at the county level. These indicators are listed below: % of county roads that have been made "climate resilient" or that are not considered to be vulnerable % of new hydroelectric projects in the county that have been designed to cope with climate change risk % of population by gender in areas subject to flooding and/ or drought in the county who have access to KMD information on rainfall forecasts % of people by gender in the county permanently displaced from their homes as a result of flood, drought or sea-level rise % of poor farmers and fishermen in the county with access to credit facilities or grants % of total livestock numbers killed by drought in the county % of area of natural terrestrial ecosystems in the county that have been disturbed or damaged % water demand that is supplied in the county % of poor people by gender in drought prone areas in the county with access to reliable and safe water supplies Number of ministries at county level that have received training for relevant staff on the costs and benefits of adaptation, including valuation of ecosystem services. Readers are referred to Annex 6 for detailed information on each of these indicators. 1

9 Bottom-up vulnerability indicators There are a wide range of ad hoc adaptation projects, carrying out valuable work at local level in reducing vulnerability. However, linking indicators to an uncoordinated set of local level adaptation activities will not create a sufficiently broad set of vulnerability indicators for the KCCAP. Instead, vulnerabilities that were flagged by stakeholders during the stakeholder consultations were used. The vulnerabilities relate to: rainfall variability and drought; heavy downpours and flooding; sea level rise; and hailstorms and frosts. In total, 62 county level indicators measuring vulnerability were derived. The county level indicators are a mixture of process-based and outcome-based indicators. Based on these county level indicators, a total of 27 national level, outcome-based indicators were identified, subsequently reduced to a shortlist of 10. The idea is that these indicators measure the effectiveness of local and county initiatives to reduce vulnerability at the national level. A large proportion of the national level indicators are taken from Vision These are relevant to adaptation because of the close alignment of adaptation with development goals. The indicators are listed below: Number of people by gender permanently displaced from their homes due to drought, flood or sea level rise Number of hectares of productive land lost to soil erosion % rural households with access to water from a protected source % urban households with access to piped water Cubic meters per capita of water storage % of land area covered by forest % of classified roads maintained and rehabilitated Number of urban slums with physical and social infrastructure installed annually Number households in need of food aid Number of County Stakeholder Fora held on climate change. Readers are referred to Annex 6 for detailed information on each of these indicators. One of the difficulties of developing an adaptation M&E framework in parallel with the other constituent parts of the KCCAP is that there is still great uncertainty over adaptation actions that are to be monitored. The adaptations proposed by SC3 in the ATAR are provisional and will be further refined before incorporation into the NAP. The knock-on effect of this uncertainty is that no actions have been agreed with MDAs responsible for the adaptation actions. Consequently, not performance related targets can be agreed. Therefore, the relevance of the process-based indicators will depend on the adaptation actions that are finally agreed. We expect the long-lists in this report to be used as a reference source. In contrast, the 20 outcome-based indicators identified above for use at national and county level are robust and useful measures of adaptation performance for Kenya, regardless of the specific adaptation actions taken. Adaptation M&E spans multiple sectors. The approach used has been to work within current institutional frameworks and those proposed by MED and SC2, so that adaptation M&E is mainstreamed within normal M&E activities across government. These institutions are, in turn, linked to the new NPBMF arrangements set out in the MRV+ report. Unfortunately, many of the institutions involved in the adaptation M&E framework do not yet exist. Until they are up and running, it is difficult to envisage how adaptation M&E will be delivered in practice. Kenya is endowed with a large number of data sets that are of considerable value to adaptation M&E. These are scattered across departments in various ministries. With the exception of data that have been processed by the Kenyan National Bureau of Statistics, which employs rigorous quality control processes, the quality of most data sets is unknown. 2

10 Data for the measurement of adaptation indicators need to be collected, collated and quality controlled. They need to be stored in a central Climate Change Relevant Data Repository. These processes are described in the MRV+ Report. The institutional M&E frameworks that have been proposed for Kenya have potential for serving an evaluation function, but the institutional roles defined thus far focus more on monitoring than evaluation. It has not been possible, at this stage, to cover issues relating to evaluation of adaption actions. These should be addressed as soon as adaptation actions are owned and associated targets have been set. Evaluation of performance against these targets will be a key part of the implementation of the KCCAP. The lack of both human and financial resources invested in M&E activities across the ministries means there is a currently a lack of capacity to provide basic M&E services. Until there is incentive within the ministries to take M&E seriously, for example by inclusion of adaptation output and outcome indicators in performance contracts, the situation is unlikely to change. Under current conditions, an adaptation M&E framework cannot function. It is impossible to overstate the importance of an effective NPBMF in further evolution of the KCCAP. The GoK, bilateral aid agencies and other providers of finance all need the results of the NPBMF to demonstrate the effectiveness of adaptation funds they provide. Therefore, without an operational NPBMF, financial support for the implementation of the KCCAP is unlikely to continue. To develop further the adaptation M&E framework, the implementation of the following components of the NPBMF road map are strongly recommended: 1. M&E of Institutional Adaptive Capacity Indicators: the aim is to demonstrate effective adaption M&E within a key MDA, such as Forestry and Wildlife, Water and Irrigation, or Agriculture/ Livestock/ Fisheries, in order to facilitate roll-out across all relevant MDAs. 2. M&E of Vulnerability Indicators: the aim is to demonstrate the effective adaptation M&E within a suitable county, in order to facilitate roll-out across all counties. 3. M&E Adaptation Data Tracking and Mapping: the aim is to map data flows required for reporting of outcome-based indicators for national level reporting. This process will generate baseline measurements for all outcome-based indicators. 4. Effectiveness Assessment of Adaptation at the County level: the aim is to show how adaptation M&E can be used to guide collective action for local climate adaptation, and to assess how well collective action on climate adaptation benefits the climate vulnerable poor. It is envisaged that the GoK will guide the selection of host institutions for these components. 3

11 2. Introduction The main objective of the sub-component 6 (SC6) of the Kenyan Climate Change Action Plan (KCCAP) is to develop a National Performance and Benefit Measurement Framework (NPBMF). The NPBMF is an integrated framework for monitoring, evaluating and reporting results of mitigation actions, adaptation actions and the synergies between them. The work of SC6 was divided into two stages. The objective of Stage 1 was to identify the building blocks for the framework. Stage has been completed (see the Stage 1 report). The objective of Stage 2 is to design the framework. This report covers the design of the adaptation components of the framework, focusing on the development of adaptation indicators and an accompanying monitoring and evaluation (M&E) framework that sits within the Government of Kenya. Without a means of monitoring and evaluating progress, it is impossible to assess the effectiveness of investment in adaptation, or to determine whether the money is being spent in the right areas. M&E are often viewed as optional, add-on activities and are therefore neglected in strategies and action plans. However, effective implementation over the duration of a strategy is highly dependent on the internal feedback generated by M&E. It is to the credit of the GoK that such a high priority has been placed on the issue in the KCCAP. Furthermore, the continuance of international funding for adaptation depends on M&E. Bilateral aid agencies, multilateral development banks and other providers of finance need the results of M&E activities to demonstrate the effectiveness of funds they provide. Therefore, further financial support for the implementation of the KCCAP will be dependent on the establishment of the framework. 2.1 Building blocks In the Stage 1 report produced by SC6, a number of challenges were identified for Kenya with regard to the design and measurement of adaptation indicators. There are no specific M&E systems designed for adaptation in operation in Kenya. However monitoring of development indicators in some sectors such as agriculture and water M&E captures adaptation information. Indicators on climate change arewere found in existing M&E frameworks in an adhoc manner rather than as part of a strategic design. Indicators on climate change are scattered throughout different departments and remain unconsolidated hence the basis of the development of the NPBF. Agencies such as NEMA are mandated to report to UNFCCC on climate change. However a systematic and structured system of collecting this information would greatly strengthen this effort The Government of Kenya already has more than 6,000 indicators that it is measuring (or trying to measure). There is substantial variation in the quality of indicators and reporting systems between departments. Communication and information flow from the counties where the most vulnerable communities are to the national level can be strengthened. In addition, the following recommendations were made for the design of an adaptation M&E framework: The M&E framework needs to be based on an integrated (rather than risk-based) approach to adaptation, combining measurement of both national and count level indicators. 4

12 The design of a national adaptation M&E framework should measure progress towards increasing the resilience to climate change of some of the most vulnerable in society, as well as measuring progress at a national level. The design of the M&E should be able, in the long-term, to accommodate community level data, in order to facilitate flow of information from communities to government. The case studies highlighted the positive role that communities can play in adaptation, but also in M&E. The Tracking Adaptation and Measuring Development approach described by Brooks et al. 1 provides useful guidance for the identification of indicator types for Kenya that fulfil these requirements. To minimise additional demands on the institutions concerned, the adaptation M&E framework needs to make as much use of existing frameworks as possible. Given the limited resources available to Kenyan MDAs and the many constraints to data collection and analysis, the selection of adaptation indicators needs to be pragmatic, rather than optimal, and the number limited. Existing indicators used in Kenya should be used as much as possible, to minimise additional workload. However, the great majority of the proposed indicators are not currently being measured. The M&E framework needs to be flexible over time. It is important to recognise that there will be changes through time, including changes to the Medium Term Plan (MTP), minor and possibly major revisions of the KCCAP, changes in the scientific knowledge and changes in the prioritisation of adaptation actions to be implemented. In Stage 2, these recommendations have underpinned design a framework for Kenya. However, as the design has evolved, one or two adjustments to the approach were necessary. These adjustments are described in the following sections. 2.2 Monitoring and evaluation policy context The design of the adaptation M&E framework needs to meet the requirements of relevant M&E policy in Kenya. A policy has been produced by the Government of Kenya to guide implementation of NIMES 2. Implementation is coordinated by the Monitoring and Evaluation Directorate (MED), which aims to improve management for development results. The key elements are summarised in Box 1.1. Box 1.1 Kenya s National Monitoring and Evaluation Policy The Policy emphasises the importance of results-based management, transparency, accountability and efficiency as fundamental principles for managing public programmes and projects in Kenya. It aims to facilitate reporting and feedback on implementation of development programmes and projects at the sub-county, county and national levels. Under the Policy, programme and project, implementers will be expected systematically to collect qualitative and quantitative data on implementation progress against the planned targets, in order provide an indication of progress made towards achieving set objectives. The monitoring process will be guided by the following: indicator identification; indicator data; frequency of data collection; responsibility for data collection; data analysis and use; 1 Brooks, N., Anderson, S., Ayers, J., Burton, I. and Tellam, I Tracking adaptation and measuring development. IIED Climate Change Working Paper No. 1, November MPND National Monitoring and Evaluation Policy. Ministry of State for Planning, National Development and Vision 2030, March

13 reporting and dissemination. Monitoring at the county and national levels will focus on assessment of progress made towards achieving the sectoral development outcomes. Evaluation will be undertaken to answer specific questions regarding performance of development interventions. The evaluations will mainly focus on why results are being achieved or not. The Policy requires that external evaluations will be conducted by an accredited and registered independent body and/or stakeholder in a participatory process; whereas internal evaluations will be conducted by the implementing agency using rapid appraisal methods. Effective M&E is based on a clear, logical pathway of results, in which results at one level lead to results at the next level. Results from one level flow towards the next level, leading to the achievement of the overall goal. If there are gaps in the logic, the pathway will not flow towards the required results. The major levels that the policy will focus on are: inputs; outputs, including processes; outcomes; and impacts. The M&E Policy therefore sets out the basic requirements for the development of indicators relating to climate change adaptation, and expectations for their use. In this report, the approach and outputs are consistent with this policy. 2.3 Definition of terms Terminology relating to M&E is defined in Annex 1. For consistency, the definitions are taken from Kenya s National Monitoring and Evaluation Policy, March Terminology relating to climate change is defined by other SCs. 2.4 Structure of this report This report is divided into 5 further sections. Section 2 explains the methodology adopted and its application. Section 3 describes the four sets of indicators that have been identified. Section 4 discusses the M&E framework that could be used for these indicators. Conclusions are drawn in Section 5 and recommendations, in the form of a number of project proposals, are set out in Section 6. 6

14 3. Methodology used for indicator selection In SC6 s Stage 1 outputs, a suitable methodology for the identification of adaptation indicators, called Tracking Adaptation and Measuring Development (TAMD) 3, was adopted. It has been developed by the International Institute for Environment and Development (IIED) and has been trialled on DFID and other projects in a number of countries with some success. The TAMD methodology describes the development of indicators that reflect vulnerability and institutional (adaptive) capacity, rather than climate impacts or risk (see Figure 2.1). By doing this, actions that focus the development end of the adaptation continuum are measured, rather than costly technological fixes that may have limited developmental benefits. The institutional adaptive capacity indicators referred to as top-down indicators and the vulnerability indicators bottom-up indicators. The meaning of these terms and how the indicators have been identified is explained in the following sections. Figure 3.1 Methodology used for identifying indicators (based on IIED) Explanation: The width of triangle indicates the number of indicators required. Processbased indicators dominate at the widest part of the triangles, outcome-based at the apex. In the Inception Report, SC6 proposed to group the adaptation indicators identified into five categories, namely: impacts, vulnerability, no regrets/low regrets, decision making, and benefits. This typology does not fit well with the TAMD approach, which proposes a different categorisation (see Figure 2.1). The proposed typology might have been usable if the adaptation actions identified by SC3 had been so categorised, but this was not the case. Given these circumstances, and the benefits of the TAMD approach, the proposed typology was not adopted. 3 Brooks et al op. cit. 7

15 3.1 Top-down indicator development The top-down indicators focus on measuring the progress towards increasing institutional adaptive capacity by government institutions from the top (national level), down to the county level. In time, the intention is that the indicators will extend (down) to the local government (location) level, but that undertaking is considered too ambitious for the current SC6 process, given the political uncertainties surrounding county government and the depth of local knowledge required for their development. According to the TAMD methodology, institutional adaptive capacity is measured from the top down because effective action to drive institutional change at county level and below depends on decisions made in the highest levels of government. The top-down indicators are driven by the adaptation actions defined by SC3. These actions are the starting point for all indicators. SC3 actions are in fact a list of projects or programmes for implementation in Kenya. The vast majority of the (over 300) SC3 actions refer to actions required by national government, across a wide range of ministries. Therefore, the associated national level indicators tend to be process-based and are equivalent to project or programme output indicators. Only a minority of SC3 actions refer to county level government or below. The assumption is that national level action will lead indirectly to a positive outcome at the county level. This will happen through the sectors, because the county governments will operate through the sectors to delivery sector priorities and plans. It is desirable to measure institutional adaptive capacity outcomes at the county level because it is at this level (and below) where adaptive capacity translates into practical benefits for the people of Kenya. Therefore, a number of outcome-based indicators have been identified that make it possible to measure performance at county level. The county level outcome indicators link directly to the national level process-based indicators. The measurement of process-based indicators at national level and outcome-based indicators at county level is counter-intuitive for government employees with experience in M&E, but is entirely logical, given the set of adaptation actions that has been proposed. Note that the outcome-based indicators are not limited to the county level; a set of national level outcome based indicators will be proposed in the next section. The thinking behind the top down indicators is represented in Figure 2.2. The process of indicator selection is summarised below in Box 2.1. Figure 2.2 The top down approach to indicator selection Sector A Sector B Sector C etc... National County The width of triangle indicates the number of indicators required. Process-based indicators dominate at the widest part of the triangle, outcome-based at the apex. Local 8

16 Box 2.1 Step-by-step process used in the identification of top-down indicators The Adaptation Technical Analysis Report (ATAR) developed by SC3 sets out a large number of indicators to be implemented by government institutions, with a focus on building institutional capacity. For NATIONAL indicators, we have: Reviewed the ATAR to identify adaptation actions for each of the 9 GoK sectors in liaison with SC3 in order to develop indicators; Identified process-based institutional indicators for these adaptations, for national level reporting; Cross-referenced each indicator to relevant adaptation actions, using the SC3 referencing system. For COUNTY indicators, we have: Identified outcome-based institutional indicators for county level reporting, based on the national indicators described above; Cross referenced each county indicator to relevant national indicators; Searched the SC6 Indicator Database for pre-existing indicators already proposed or measured by the ministries and used these in place of the proposed indicators where there is a good match. For both sets of indicators, we have: Checked the indicators against the suggested generic indicators proposed by TAMD; identified and filled the gaps; Identified the MDA that is most likely to own each indicator (in several cases, multiple MDAs would contribute to the indicator management process); Determined the likely time scale for each action and therefore the timeframe for indicator measurement. 3.2 Bottom-up indicator development Bottom-up indicators focus on measuring the progress made by MDAs, the private sector, NGOs and communities to reduce vulnerability to climate change. The idea is to measure from local level up to the national level. For this aspect of the M&E framework design, the county government is as close to the bottom as can be reached at present. In time, the intention is that the indicators will extend down below the local government (location) level, to the community level. However, the county government institutions need to be in place before the system can be thus developed. This will take place within the new Government from April According to the TAMD methodology, vulnerability is measured from the bottom up because effective action to reduce climate change vulnerability needs to take place at a local level. This is because vulnerability is spatially very variable, and therefore difficult to address through national action. For example, a wealthy agricultural district may contain pockets of very poor farmers tilling marginal land that is prone to drought or soil erosion. Because of high agricultural productivity across the district as a whole, the national response to climate change could be on technical adaptation measures, such as improved irrigation systems. Yet these solutions will not help the poor farmers who will be hit hardest by climate change. The determination of county-level vulnerability indicators was challenging. There is currently no coordinated, Kenya-wide programme of district or local level adaptation actions. There are a wide range of ad hoc adaptation projects, carrying out valuable work in 9

17 reducing vulnerability. However, linking indicators to uncoordinated adaptation activities will not create a sufficiently broad set of vulnerability indicators for the KCCAP. Instead, there is merit in focusing on climate-related vulnerabilities that were flagged by stakeholders during the stakeholder consultations. The climate impacts could be categorised as follows: Rainfall variability and drought Heavy downpours and flooding Sea level rise Hailstorms and frosts. The descriptive information from the county consultation workshops and the views derived from SC3 and SC6 workshops has provided enough information to envisage the vulnerabilities in their local context. Indicators were thus derived. The county level indicators are a mixture of process-based and outcome-based indicators. Unlike the national level institutional adaptive capacity indicators, the county level vulnerability indicators lack any specific actions to underpin them. For this reason, there is no immediate expectation of a Kenya-wide reduction in vulnerability. However, the county level adaptation plans envisaged by SC3 will eventually cascade from the NAP across all 47 counties. These, in conjunction with a range of existing local adaptation initiatives (largely driven by NGOs at this point) will, at some point, trigger sufficient local level action for progress to be measurable at the county level. There will be some scepticism over the need to measure progress on the effect of actions which have not yet been clearly specified. However, reducing vulnerability to climate change is fundamental to the overall success of the Kenyan Climate Change Action Plan. Measures that, for example, reflect the health of individuals, the sustainability of livelihoods, and the health of ecosystems and the wildlife they support, are key statistics that simply cannot be ignored. The bottom-up process facilitates the identification of a set of national level outcome-based vulnerability indicators. These national indicators reflect progress across all counties and many relate to socio-economic conditions. The national indicators link directly to the county indicators. A large proportion of the national indicators are taken from Vision These are relevant to adaptation because of the close alignment of adaptation with development goals. The thinking behind the bottom up indicators is represented in Figure 2.3. The process of indicator selection is summarised below in Box 2.2. Figure 2.3 The bottom up approach to indicator selection National County The width of triangle indicates the number of indicators required. Process-based indicators dominate at the widest part of the triangle, outcome-based at the apex. Local Sector A Sector B Sector C etc... 10

18 Box 2.2 Step-by-step process used in the identification of bottom-up indicators The ATAR identifies only a few actions to reduce vulnerability at the county level. However, reduction in county (and local) level vulnerability is an important outcome of the ATAR/ NAP. For COUNTY indicators, we have: Reviewed the outcome of the county consultations for each sector to identify problems being encountered as a result of current climate variability; Considered what kind actions are required to reduce vulnerability to this climate variability. Note that these actions are not made explicit in the ATAR, but are the logical outcome of the implementation of the NAP; Identified process-based and outcome based indicators for these adaptations, for county level reporting. Not all indicators will be relevant to all counties, so the subset used by each county will be smaller than the list produced in the report. For NATIONAL indicators, we have: Identified outcome-based or process-based institutional indicators for national level reporting based on the indicators described above; Cross referenced each national indicator to all relevant county indicators; Searched the SC6 Indicator Database for pre-existing indicators already proposed or measured by the ministries and used these in place of the proposed indicators where there is a good match. For each set of indicators, we have: Checked the indicators against the suggested generic indicators proposed by TAMD; identified and filled the gaps; Identified the MDA that is most likely to own each indicator; Determined the likely time scale for each action and therefore the timeframe for indicator measurement. 3.3 Indicator definition Clear, precise and considered wording for each indicator is crucial for three reasons: Any ambiguity in the meaning of an indicator or in its interpretation renders the indicator worthless; The wording determines the value of an indicator for measuring adaptation performance: an indicator that is defined in general terms (such as number of people with access to drinking water ) is a much less useful measure of adaptation than one that makes a link to climate or climate change or its associated impact (such as number of people with access to drinking water during drought ); The wording also determines the data collection requirements: the additional burdens placed on the M&E staff of the GoK need to be moderate, otherwise the indicator will not be measured. Indicators also need to be placed in the context of an M&E framework, so that they can be properly interpreted and used to evaluate progress. To provide this context, an indicator data sheet template has been designed (see Table 2.1). The indicator data sheet has been completed for each of the indicators proposed in Section 3. 11

19 Table 2.1 Indicator data sheet template Description Indicator (reference): Type: Level: Related action, objective or rationale for measurement: Interpretation: Unit of measurement: Method of calculation: Frequency of measurement: Baseline value/ year: Duration of measurement: Expected trend w. adaptation: Target(s): Responsible MDA: Sources of data: Additional comments: There are gaps in the completed data sheets, notably against entries for the baseline and targets. The reasons for these gaps are discussed in Section Performance and benefits SC6 has been tasked with producing a National Performance and Benefits Measurement Framework. An M&E system can be used to measure performance relative to targets (assuming targets have been sets). It will not usually measure benefits. On a number of occasions, stakeholders have asked questions such as: what are the benefits? or haven t you forgotten about the benefits in the NPBMF? The word benefits is a little ambiguous in this context. The intended meaning as revealed during the SC6 process relates to general developmental benefits, covering social, economic and environmental aspects. There is no expectation that the benefits should be quantified financially (although this could be appropriate in some circumstances). SC6 has used a broad interpretation of adaptation that covers development. This is because many of the actions proposed by SC3, such as provision of clean drinking water supplies or 12

20 improved sanitation are developmental actions. In fact, there is often a very fine line between adaptation and development in Kenya. For example in the arid and semi-arid lands (ASALs), all developmental action revolves around the drought cycles. The proposed indicators should, therefore, capture adequately the developmental benefits associated with implementation of adaptation actions. This is particularly true of the list of vulnerability indicators, which are closely related to social and economic well-being of vulnerable groups. Gender differences are captured by a number of proposed indicators that require a gender breakdown. In other words, the measurement of performance by the vulnerability indicators is also, in a very large part, a measurement of social, economic and environmental benefit. The addition of specific social, economic and environmental indicators to the list of indicators was considered, but has been rejected this for two reasons: firstly, they add unwelcome additional complexity and cost to the adaptation M&E framework; and secondly, they are already covered comprehensively in Vision 2030 and other sector-specific indicators used by the ministries. 3.5 Attribution of outcomes to actions For the top down approach, there is a direct link between the actions identified by SC3 and the national level (process-based) institutional development indicators. There is also a direct link between the national level indicators and the county level (outcome-based) indicators. Therefore, it will be possible to attribute the benefits at a county level to actions taken at the national level, to some extent. As discussed in the Stage 1 report, it will not be easy to quantify this effect, but this may be possible for some indicators (depending on the action). For the bottom-up approach, there is no direct link between actions and vulnerability indicators at this stage in the KCCAP. There is an indirect link between the SC3 actions and bottom-up county indicators, but it is too indirect for attribution to be a realistic option. However, when county level projects and/or programmes to address vulnerability have been defined by the sectors, it may be possible, in the future, to use the indicators to attribute county level actions to county level (process-based) indicators and thus to national level (outcome-based) indicators. The issue of attribution has not been considered further because: a) it is not yet clear which of the 300 plus adaptation actions proposed by SC3 will be taken forward for implementation in the final NAP; and b) the actions themselves are not specified in a way which makes attribution a simple process. 13

21 4. Proposed indicators In this section the indicators identified for climate change adaptation are set out and an explanation of their refinement and prioritisation provided. In Section 3.1 the top-down (institutional adaptive capacity) indicators are described and in Section 3.2 the bottom-up (vulnerability) indicators are described. These indicators have been developed through the application of the methodology elaborated in Section Top-down Institutional Capacity Indicators Given that there are over 300 adaptation actions proposed in the National Adaptation Technical Analysis (ATAR), it was inevitable that a large number of indicators would be required. However, it was not necessary to identify over 300 indicators. In some cases, it was possible to identify indicators that covered a number of proposed actions. In others, the actions were described in very general terms and it was not possible to identify a useful indicator. In total, 62 national level, process-based indicators were identified. These are listed in Annex 2. The list of actions in the ATAR will be significantly reduced prior to the production of the NAP. The number of indicators actually required could be significantly less than the 62 identified here. Therefore, a shortlist of the national level indicators was not produced. Prioritisation of these indicators is not possible until the content of the NAP has been agreed. A total of 28 county level, outcome based indicators were identified, which are based on the national level indicators. These are listed in Annex 3. In response to comments from the MEMR/ CCS and other stakeholders consulted, a more manageable shortlist of 10 county level indicators has been produced. The selection was based on an assessment that considered the following criteria: The measurability of the indicator. Those indicators that could be measured using few data sets and for which data are/ could be easily accessible were favoured over those that did not have these characteristics. The scope for cross-sectoral benefits from the adaptation actions underpinning the indicator. For example, a climate resilient road network was considered particularly important because of its importance to the economy in general, in particular the movement of agricultural produce to market and tourism. The number of process indicators covered. Indicators that could be used to reflect progress on a large number of the national (process) indicators that underpinned them were favoured. The number of Kenyans that could potentially benefit from adaptation actions underpinning the indicator. Indicators measuring progress on institutional adaptive capacity that have the potential to benefit large numbers of people were favoured. A semi-quantitative scoring system for applying these criteria was used. Each indicator was given a score of between 1 and 4 for each criterion. Each criterion was equally weighted. The assessment was informed by and checked with SC3 to ensure it reflected their views and requirements. The selected indicators are shown in Table 3.1. Completed indicator data sheets are listed in Annex 6. 14

22 Table 3.1 Selected county level institutional adaptive capacity (top-down) indicators Ref. no. T.2 T.3 T.5 T.8 T.12 Proposed county level indicator % of county roads that have been made "climate resilient" or that are not considered to be vulnerable [2,3,4,5,6] % of new hydroelectric projects in the county that have been designed to cope with climate change risk [7,8,9,10,11] % of population by gender in areas subject to flooding and/ or drought in the county who have access to KMD information on rainfall forecasts [12,13,14,15,16,20] % of people by gender in the county permanently displaced from their homes as a result of flood, drought or sea-level rise [21,22,23] % of poor farmers and fishermen in the county with access to credit facilities or grants [31] T.13 % of total livestock numbers killed by drought in the county [32,33,41] T.16 % of area of natural terrestrial ecosystems in the county that have been disturbed or damaged [43,44,46] T.19 % of water demand that is supplied in the county [23,44,48,56] T.21 T.28 % of poor people by gender in drought prone areas in the county with access to reliable and safe water supplies [23,44,50,56] Number of ministries at county level that have received training for relevant staff on the costs and benefits of adaptation, including valuation of ecosystem services 62,63] The prefix T stands for top-down. The reference numbers enable the indicator to be located in Annex 3. The numbers in [square brackets] are the reference numbers for national level indicators in Annex 2 to which these county level indicators relate. 4.2 Bottom-up Vulnerability Indicators In total, 62 county level indicators were identified. These are a combination of process-based and outcome-based indicators. The aim is to reflect the outcome of local level actions within the county (hence the outcome-based indicators) and to measure progress on county level initiatives (hence the process-based indicators). These indicators are listed in Annex 4. The indicators in the county level list reflect vulnerabilities that relate to climate impacts experienced across Kenya. Nevertheless, they are generic. Not all indicators will be relevant to all counties. Furthermore, it may not be possible to implement the all the necessary actions to reduce vulnerability due to the cost of implementation. The number of indicators actually required could be significantly less than the 62 identified here. Therefore, a shortlist of the county level indicators has not been produced. Prioritisation of these indicators is not possible until county governments have been established and relevant adaptation actions financed. A list of 28 national level, outcome based indicators, which are based on the county level indicators, has been produced. The indicators are listed in Annex 5. Note that a large proportion of these indicators are taken from the list of Vision 2030 indicators. The Vision 2030 indicators matched the proposed indicators closely and are highly relevant. By using existing indicators, the additional M&E workload can be significantly reduced. 15

23 In response to comments from the MEMR/ CCS and other stakeholders consulted, a more manageable shortlist of 10 national level indicators has been produced. The selection was based on an assessment that considered the following criteria: The measurability of the indicator. Those indicators that could be measured using few data sets and for which data are/ could be easily accessible were favoured over those that did not have these characteristics. The scope for cross-sectoral benefits from the adaptation actions underpinning the indicator. For example, the number of people made permanently homeless due to climate events was considered particularly important because of health risks, loss of economic productivity and the additional pressures put on urban infrastructure due to rural urban-migration. The number of process indicators covered. Indicators that could be used to reflect progress on a large number of the national (process) indicators that underpinned them were favoured. The number of Kenyans that could potentially benefit from adaptation actions underpinning the indicator. Indicators measuring progress on efforts to reduce vulnerability that have the potential to benefit large numbers of people were favoured. The selection of the top-down indicators was based on the same semi-quantitative scoring system as described in Section 3.1. The selected indicators are shown in Table 3.2. Completed indicator data sheets are listed in Annex 6. The list of indicators in Table 3.2 includes reference to recent climate trends (which were flagged in the county consultations) that are covered by each indicator. Not all the climate trends identified are observed in all counties, so this information will be helpful for selection of relevant indicators at county level. 16

24 Table 3.2 Selected county level vulnerability (bottom-up) indicators Ref. no. Proposed national level indicators RVD HRF SLR HF B.1 B.4 B.7* B.8* Number of people by gender permanently displaced from their homes due to drought, flood or sea level rise [1,4,10,13,14,18,45,46,47] Number of ha of productive land lost to soil erosion [4,6,7,12,17] % rural households with access to water from a protected source [19,20,22] % urban households with access to piped water [19,20,22] B.9* Cubic meters per capita of water storage [18,19,20,22] Y Y Y Y B.11* % of land area covered by forest [18,19,20,23,24,25] Y Y B.17* B.18* B.25* B.27 % of classified roads maintained and rehabilitated [33,34,35] Number of urban slums with physical and social infrastructure installed annually [21,30,36,37] Number households in need of food aid [1,4,10,13,14,18,45,46,47,54,55] Number of County Stakeholder Fora held on climate change [58,59,60,61,62] Y Y Y Y Y Y Y Y Y Y Y Y Y The prefix B stands for bottom-up. The reference numbers enable the indicator to be located in Annex 5. The numbers in [square brackets] are the reference numbers for county level indicators in Annex 4 to which these national level indicators relate. Key: RVD HRF SLR HF Increase in rainfall variability and drought Increase in heavy rainfall and floods Sea level rise Increase in occurrence of abnormally large hailstones/ frost in montane areas 4.3 Sub-sector coverage The multi-sectoral nature of the adaptation challenge is highlighted in Table 3.3. Clearly, if progress is to be made on measuring the performance towards adaptation across all these sectors, the involvement and support of a wide range of ministries in the Government of Kenya is required. The often held view that climate change is an environmental issue and should be addressed by an environmental ministry is clearly misguided. The reduction in the numbers of outcome indicators in the two short-lists means that some sectors go unrepresented. The focus in the short-lists falls on sectors that are most at risk from climate change. Table 3.3 demonstrates success in this endeavour. 17