Community Based Measuring and Prioritizing Adaptation Actions in Agriculture Sector of the Gangetic Basin SVRK Prabhakar With study partners in Bangladesh, India and Nepal Institute for Global Environmental Strategies Japan Presented to the visiting JICA Delegation to IGES. 12 May 2014, IGES, Hayama, Japan. This work is supported by the "Environment Research and Technology Development Fund" of the Ministry of the Environment, Japan
Outline Project on adaptation metrics Background to adaptation metrics Determinants, criteria and types of adaptation metrics Adaptation metrics in Agriculture Methods to identify metrics Suggested metrics Our approach Quantitative approach for measuring adaptation effectiveness through developing LaIn Qualitative approach for prioritizing adaptation actions using AHP Way Forward 2
Project Background Project on ‘Adaptation Metrics’, with funding from Suishinhi (S8-3-4) Objectives To identify suitable adaptation metrics for agriculture sector in the Gangetic Basin To identify suitable adaptation decision making frameworks for operationalizing adaptation metrics Methodology Literature review Expert consultation and policy dialogues Questionnaires (web, Climate L) and field visits Multi-criteria analysis for bottom-up indicators 3
Study Location Map source: Ministry of water resources, http://mowr.gov.in/map.asp?langid=1 Kanpur Dehat, India Bara and Parsa, Nepal Chapai Nawabganj, Bangladesh Boundary of Gangetic Basin (Approx) 4
Motivation Behind this Work 5
Need for Metrics: BAP on Adaptation (Section c, i-v) and subsequent texts “Enhanced action on adaptation with consideration of … prioritization of actions …and support adaptation in a coherent and integrated manner ” “ Positive incentives for developing countries for enhanced mitigation and adaptation actions ” 6
How to Prioritize and Incentivize Adaptation Actions? By Knowing how much ‘adaptation’ we want to achieve in a project/program Knowing where we want to go (adaptation targets?) Setting a time frame This is facilitated by Setting a base line of adaptation (to compare the progress and effectiveness) And agreeing on a measurement system (adaptation metrics) 7
Adaptation Metrics: Mitigation vs Adaptation Mitigation Adaptation Has a protocol (KP) that governs No ‘protocol’ to govern adaptation There are GHG reduction There are no ‘adaptation targets to meet with coordinated targets ’ to meet efforts Ways and means to measure No streamlined measurement the impact of collective actions system for adaptation Global actions and global benefits Mostly local actions and local (more organized at global level) benefits (with some undeniable global spillover benefits) Physical principles that govern At nascent stages: Complex mitigation interaction of biophysical and socioeconomic elements 8
And…in addition Adaptation deals with systems that are at different levels of adaptive capacity Several adaptation options deferring in their effectiveness and outcomes 9
Adaptation Metrics Metric: A system of measurement The unit of measurement Value of the unit 10
Advantages of Adaptation Metrics Ability to measure adaptation at any given point of time Provide a means to compare the level of adaptation reached across locations, regions, societies and nations Help in decision making related to identification and prioritization of appropriate adaptation actions and for funding Help track the progress over the time scales Help in minimizing the risk of mal-adaptation 11
Criteria for Adaptation Metrics Measurable Cost effective Scalable Comparable Across time and geographical scales Context specific Specific to system being measured Sensitive to degree of adaptation Learning and evolving 12
Different metrics Qualitative and quantitative Cost and time resources, effectiveness Direct and proxy To accommodate those cannot be directly measured Ex-ante vs. Ex-post To chose options and to measure outcomes Local vs National To accommodate differential impacts of climate change at different scales 13
Methods for Choosing Adaptation Metrics in Agriculture Methodology Geographical Scope Source Benefit-cost analysis Local (L), national Tubiello and Rosenzweig, 2008 (N) and regional (R) scales Cost-effectiveness analysis L,N,R Rosenzweig and Tubiello, 2006 Multi-criteria analysis L,N,R Dolan et al., 2001 Expert consultation (workshops) L,N,R Rosenzweig and Tubiello, 2007 Dynamic crop models L,N,R Tubiello and Rosenzweig, 2008 Modelling relationship between L Luers et al., 2003 stressor and outcome variables GIS based index based on Sub-national Swanson et al., 2007 normalization and aggregation of determinants Historical trend analysis and Sub-national Allison and Hobbs, 2004 constructing conceptual models Prabhakar, 2012
Some Suggested Adaptation Metrics Metric/s Reference Description on availability and limitations (includes authors judgement) Mean and variability of yield Tubiello and Measured and computed metrics. Available at local, national, and production, income, Rosenzweig, 2008 regional and international levels in many countries. The aggregate of value added aggregate of value added may need to be computed at the local level as such statistics will not be readily available. Nutrition index Tubiello and Computed metric (sum of local production and net imports Rosenzweig, 2008 divided by total food demand). Can be computed at national and regional level. Yield estimates (remotely Luers et al., 2003 Estimates could help in filling the gaps in the existing yield sensed), yield variability, data, validating the measured yield data etc. Accuracy could highest relative yield/yield be an issue when resolution of remote sensing is low. percentile Agricultural export, farm Venema, 2006 Agricultural exports and out-migration of farming are mostly income, out-migration from applicable at the macro-economic level, while data on rest of farming, emergency the metrics (emergency payments) could be sparingly payments available. Sources of income, livestock Brooks and Adger, It was not clear on how many sources of income is considered number, source of fertilizer 2005 as optimal, and also the number of cattle. However, it is suggested that the higher the sources of income, with more diversification into non-farm sources, the higher the adaptive capacity. Prabhakar, 2012
Problems with Earlier Suggestions Mostly single metrics and doesn’t often provide an overall picture of adaptation in agriculture sector Policy makers may often prefer single composite index representing the entire sector with a single number (not withstanding their intrinsic limitation) 16
Our Approach 17
Research Methodology Quantitative approach for quantifying adaptation through local adaptation index (LaIn) Qualitative approach for prioritizing adaptation options: Multi-criteria analysis (MCA) using analytical hierarchy process (AHP) 18
Quantitative Approach Local Adaptation Index (LaIn): Localizing GaIn GaIn: Comprehensive macro assessment of Vulnerability and Readiness of a country in a given year LaIn: Ultimate objective: Precise assessment of Vulnerability and Readiness at a given point of time (ex- ante and ex-post) at village level 19
LaIn vs GaIn Same analytical framework Re ad . Index Mean ( Index ) i all i * Weight / Max ( Score ) * 60 Index all Stdev ( Index ) i all i Re . ad Vu ln . Index Mean ( Index ) i all i * / ( ) * 40 Weight Max Score Index all Stdev ( Index ) i all i Vu ln . 20
Framework for Assessing the Effectiveness of Adaptation Action Alternative Ac 3 Scenario Adaptive Capacity Ac 2 Ac 1 I z Ac 0 I x I y BAU scenario T1 T2 T3 T4 Time Where: Ae x Ac Ac Ae x : Effectiveness of adaptation action x; 1 0 Ac 0 , Ac 1 : Adaptive Capacity at times T1 and T2 Ix, Iy, Iz: Interventions x, y, z 21
Review Literature for identifying indicators, Regional Consultation Indicator vetting through Participatory Appraisal Processes Focused group discussions and ranking of indicators and criteria with researchers, local administration, and NGOs etc in each project country in GMS region Developing draft questionnaires for inputs from communities, local administration, NGOs and researchers Conduct pilot questionnaire surveys to test the usability of questionnaires Conduct actual surveys for identifying local effectiveness indicators Participatory ranking of indicators and criteria Quantification of indicators Incorporation of local effectiveness indicators into GaIn computation for arriving at LaIn Conduct consultations with local admin and NGOs etc to identify strengths and weaknesses for mainstreaming LaIn into their decision making process Integrating LaIn into local decision 22 making mechanisms
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