Climate change vulnerability and capacity assessments (VCA) assume primary importance in adaptation planning as they help governments, funding agencies ...
2015 NABARD 1
Accompanies an Excel-based tool of Vulnerability and Capacity Assessment Index (VCAI) for Climate Change Adaptation of Natural Resourcebased Communities
2015
National Bank for Agriculture and Rural Development, Mumbai, India
National Bank for Agriculture and Rural Development Mumbai, Maharashtra, India
Suggested citation: Prabhakar, S.V.R.K. (2015) Methodology and Guidelines for Vulnerability and Capacity Assessment of Natural Resource-based Communities for Climate Change Adaptation. NABARD and Adapt Asia-Pacific Joint Technical Report. Mumbai, India: NABARD and Adapt Asia-Pacific.
Author: Sivapuram V.R.K. Prabhakar
© 2015 National Bank for Agriculture and Rural Development
Unless otherwise stated in this document, no part of this document may be reproduced or transmitted in any form by any means without the written authorization from NABARD.
CONTENTS FOREWORD ..............................................................................................................................................III PREFACE AND ACKNOWLEDGEMENTS........................................................................................ IV TABLES ....................................................................................................................................................... V FIGURES .................................................................................................................................................... VI ACRONYMS ............................................................................................................................................. VII 1. INTRODUCTION ................................................................................................................................... 1 2. THE CONTEXT ...................................................................................................................................... 5 2.1 VULNERABILITY ASSESSMENT REQUIREMENTS BY THE ADAPTATION FUND............................................... 5 2.2 OBSERVATIONS FROM AF APPROVED PROJECTS ......................................................................................... 6 2.3 VULNERABILITY ASSESSMENT REQUIREMENTS OF NABARD .................................................................... 9 3. UNDERSTANDING CLIMATE CHANGE VULNERABILITY .................................................. 15 3.1 THE CONCEPT OF CLIMATE CHANGE VULNERABILITY ................................................................................ 15 3.2 VULNERABILITIES AS COMMON DENOMINATORS ..................................................................................... 20 3.3 VULNERABILITY THRESHOLDS .................................................................................................................. 22 3.4 VULNERABILITY ASSESSMENT METHODOLOGIES AND TOOLS ................................................................... 23 4. THE VULNERABILITY AND CAPACITY ASSESSMENT INDEX (VCAI) ........................... 33 4.1 VULNERABILITY ASSESSMENT REQUIREMENTS OF NABARD ................................................................. 33 4.2 THE PROCESS ............................................................................................................................................ 34 4.3 OBJECTIVES OF THE VCAI TOOL .............................................................................................................. 35 4.4 METHODOLOGY FOR COMPUTING THE VCAI ........................................................................................... 36 5. GUIDELINES FOR USING VCAI .................................................................................................... 47 5.1 DESCRIPTION OF THE VCAI TOOL ............................................................................................................. 47 5.2 THE PLACE OF VCAI TOOL IN A PROJECT .................................................................................................. 64 5.3 INDICATOR PRIORITIZATION...................................................................................................................... 65 5.4 WEIGHTS FOR INDICATORS AND SECTORS ................................................................................................. 65 5.5 UNIT OF VULNERABILITY ASSESSMENT .................................................................................................... 66 5.6 SAMPLE SIZE .............................................................................................................................................. 66 5.7 COLLECTING DATA FOR INDICATORS ......................................................................................................... 66 5.8 GENDER CONSIDERATIONS........................................................................................................................ 67 5.9 FILLING IN INDICATOR VALUES ................................................................................................................. 69 5.10 INTERPRETING THE VCAI OUTPUTS........................................................................................................ 69 5.11 USING VCAI FOR M&E ............................................................................................................................ 70 5.12 WHAT IS NEXT AFTER VULNERABILITY ASSESSMENT?............................................................................. 71 6. MOCK EXERCISE ............................................................................................................................... 75 6.1 CONTEXT .................................................................................................................................................... 75 6.2 PARTICIPATORY EXERCISE ........................................................................................................................ 75 6.3 CALCULATIONS .......................................................................................................................................... 77
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6.4 INTERPRETATION OF RESULTS .................................................................................................................. 82 7. CONCLUSIONS .................................................................................................................................... 83 8. REFERENCES ...................................................................................................................................... 84 9. ANNEXURES ....................................................................................................................................... 89 ANNEXURE 1: CLIMATE CHANGE IMPACTS AND VULNERABILITIES IN INDIA................................................. 91 ANNEXURE 2: IMPORTANT GLOSSARY OF TERMS ........................................................................................... 97 ANNEXURE 3: COMMUNITY CONSULTATIONS FOR VETTING INDICATORS ..................................................... 99 ANNEXURE 4: LIST OF PROXY INDICATORS .................................................................................................. 110 ANNEXURE 5: LIST OF INDICATORS FOR POLICY AND INSTITUTIONAL ASPECTS.......................................... 112 ANNEXURE 6: STATISTICAL SAMPLE SIZE ..................................................................................................... 114 ANNEXURE 7: PRA TECHNIQUES FOR VCAI ................................................................................................. 115 ANNEXURE 8: ADAPTATION DECISION MAKING USING MULTI-CRITERIA METHODOLOGIES ..................... 125
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FOREWORD The agriculture sector plays an important role in the livelihoods and wellbeing of millions of people and in the economy of India. Emerging evidence indicates that the country’s agriculture sector and the livelihoods of millions are at stake due to climate change. Urgent interventions are required to improve the adaptive capacity of agriculture communities and, to this effect, the National Bank for Agriculture and Rural Development (NABARD) has been making strides to put in place robust mechanisms to fund and implement a series of projects designed to reduce the climate change vulnerability of the country’s agriculture sector. NABARD has been identified as the National Implementation Entity (NIE) by the Adaptation Fund Board (AFB). In its NIE role, NABARD encourages various Executing Entities (EEs) to design and submit proposals and implement projects addressing climate change vulnerability in India. In order to provide a uniform benchmark for projects to assess baseline vulnerabilities and project performance, NABARD has partnered with USAID Adapt Asia-Pacific to develop a vulnerability and capacity assessment (VCA) methodology. We hope that this methodology will help all EEs to assess vulnerabilities and implement adaptation projects successfully. We welcome any feedback to improve the methodology and approach developed. Please note that this document accompanies an Excelbased tool of Vulnerability and Capacity Assessment Index (VCAI) for Climate Change Adaptation of Natural Resource-Based Communities. NABARD
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PREFACE AND ACKNOWLEDGEMENTS Adaptation to climate change has emerged as an important developmental concern for India. Adaptation finances are on the rise continuously, both from global and national sources. Therefore it is essential that the agencies engaged in the planning and execution of adaptation projects and programs have a set of tools made available for successful implementation of climate change adaptation (CCA) interventions. Vulnerability and capacity assessment (VCA) is an important step for implementing CCA, helping stakeholders to pinpoint specific vulnerabilities to be addressed. While there are currently several VCA tools available, it is often difficult for national and subnational entities to take stock of these tools and identify an effective option for their own use so this work aims to address this need. The National Bank for Agriculture and Rural Development (NABARD) has been identified as a National Implementation Entity (NIE) by the Adaptation Fund Board (AFB). As an NIE, NABARD needs to guide the Executing Entities (EEs) in identifying and addressing climate change vulnerabilities in all the projects. Keeping this need in view, USAID Adapt Asia-Pacific has collaborated with NABARD to develop this simple VCA tool using participatory approaches. As well as helping EEs determine the baseline vulnerabilities, it can also be used to assess project effectiveness in terms of vulnerability reduction on a project timeline, evaluate and compare interventions for their vulnerability reduction potential, and to prioritize regions. This work would not have been accomplished without encouragement and guidance provided by Mr. Lee Baker and Mr. Amitabha Ray of USAID Adapt AsiaPacific and Mr. B.G. Mukhopadhyay, Mr. V. Mashar, Mr. S.V. Kamble, Mr. S.K. Dora of NABARD. We also acknowledge the helpful inputs provided by numerous local communities, EEs and staff associated with NABARD and experts who participated in consultations. SVRK Prabhakar Author iv
TABLES Table 1: Nature of vulnerability elements in various project proposals submitted to NABARD ................................................... 10 Table 2: Climate change vulnerability and risk assessment parameters considered by different organizations ....................... 27 Table 3: Comparison of various vulnerability assessment approaches in the Indian context ................................................................................. 28 Table 4: List of physical exposures included in the VCAI tool ... 39 Table 5: Sensitivity and capacity indicators for different sectors used in the VCAI tool ........................................................................ 40 Table 6: List of PRA techniques that can be used for conducting VCAI .................................................................................................... 67 Table 7: Gender-specific vulnerability indicators ......................... 68 Table 8: Exposure of Blocks Place 1 and Place 2 .......................... 78 Table 9: Sensitivity of Blocks Place 1 and Place 2 ......................... 79 Table 10: Capacities of Blocks Place 1 and Place 2 ....................... 80 Table 11: Sensitivity and Capacity of Blocks Place 1 and Place 2 81 Table 12: VCAI values by area for Blocks Place 1 and Place 2 .... 81
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FIGURES Figure 1: Evaluation of Adaptation Fund projects on addressing vulnerabilities ......................................................................................... 7 Figure 2: Relation between vulnerability, adaptive capacity and net impacts (Prabhakar, 2013) ........................................................ 18 Figure 3: From vulnerable situation to resilient and adaptation situation .............................................................................................. 19 Figure 4: Synergies between CCA, DRR and SD........................... 21 Figure 5: Relationship between vulnerability, determinants of adaptive capacity and impacts (Prabhakar and Srinivasan, 2010) .............................................................................................................. 23 Figure 6: Climate change impact pathway for agriculture and macro-economic linkages (Prabhakar and Srinivasan, 2010) .... 25 Figure 7: Sequence of steps involved in developing the vulnerability assessment methodology for use by NABARD ...... 35 Figure 8: Screenshot of the Overview worksheet ......................... 50 Figure 9: Screenshot of the Exposure worksheet ......................... 54 Figure 10: Screenshot of Input_Sensitivity and Capacity worksheet ........................................................................................... 56 Figure 11: Screenshot of Input_Computations worksheet .......... 58 Figure 12: Graphical output of exposer in the Output worksheet .............................................................................................................. 60 Figure 13: Graphical output of sensitivity, capacity and vulnerabilities in different sectors .................................................. 61 Figure 14: Graphs comparing overall rankings of places ............ 62 Figure 15: Data output provided by the VCAI tool ....................... 63 Figure 16: Steps involved in developing adaptation strategy ..... 64 Figure 17: Employing VCAI for project M&E purposes ............... 70 Figure 18: Unified process for conducting vulnerability assessment and prioritization of adaptation options ................... 73
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ACRONYMS AF
Adaptation Fund
AHP
Analytic Hierarchy Process
CBA
Community based adaptation
CCA
Climate change adaptation
CEDRA
Climate change and Environmental Degradation Risk and Adaptation assessment
CRED
Center for Research on the Epidemiology of Disasters
CRiSTAL
Community Based Risk Screening Tool
DRI
Disaster Risk Index
DRR
Disaster risk reduction
EE
Executing Entities
EM-DAT
Emergency Events Database
FGD
Focus group discussions
GIS
Geographical Information Systems
HadRM2
Hadley Centre Regional Model 2
IPCC
Intergovernmental Panel on Climate Change
M-BRACE
Mekong-Building Resilience to Climate Change in Asian Cities
MCA
Multi-criteria Analysis
NABARD
National Bank for Agriculture and Rural Development
NAPA
National Adaptation Programs of Action
NIE
National Implementation Entity
PRA
Participatory rural appraisal
SD
Sustainable development
SPREP
Secretariat of the Pacific Regional Environment Program
SWAT
Strengths, weaknesses, opportunities and threats analysis
UNDP
United Nations Development Program
VCA
Vulnerability and capacity assessment
VCAI
Vulnerability and Capacity Assessment Index for climate change adaptation of natural resource-based communities
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1. INTRODUCTION Climate change will have considerable impact on key sectors such as agriculture, water, natural ecosystems, biodiversity and health in India.
Climate change is projected to have serious implications on the developmental aspirations of countries in the Asia-Pacific region. India is projected to experience a net negative impact from climate change because a large proportion of its population is dependent on climate sensitive sectors. Additionally, existing vulnerabilities will be exacerbated by climate change. For example, ‘Climate Change in India: A 4x4 Assessment Report’, a significant report by the Indian Network for Climate Change Assessment, has stated that climate change will have considerable impact on key sectors such as agriculture, water, natural ecosystems, biodiversity and health (Ministry of Environment and Forest, 2010). The report also stated that India is the country most vulnerable to climate change globally, due to a mix of factors that include a long coastline, heavy dependency on glacier-fed river systems, a high rural population that depends on agriculture and allied occupations as its primary livelihood, as well as significant rural and urban poverty.1 Several funding opportunities, both international and national, have emerged to help countries address climate change vulnerabilities and put in place robust adaptation plans, programs, policies and projects at all levels. At the same time, it has also been widely recognized that developing countries need additional assistance to build capacities to effectively tap into these funding opportunities. These countries also require help to design and implement adaptation projects on a scale that is necessary to address climate change adaptation (CCA) planning needs. Addressing widespread vulnerabilities with the available funding, which is often limited compared to the funding needs (Prabhakar, 2011), requires that the adaptation interventions are implemented where they are necessary and on a priority basis. This can only be done through providing appropriate tools that help stakeholders engaged in implementing adaptation projects and programs to prioritize regions and sections of the society that need to be targeted. 1
For more discussion on the climate change vulnerability of India, please refer to Annexure 1.
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Vulnerability assessments are important for adaptation planning for helping stakeholders prioritize areas, communities and projects and help allocate resources for adaptation.
Climate change vulnerability and capacity assessments (VCA) assume primary importance in adaptation planning as they help governments, funding agencies and Executing Entities (EEs) to prioritize geographical areas, vulnerable communities and projects whilst keeping in view the limited resources available for investment in adaptation. In addition, VCAs would provide the means to measure the progress in achieving adaptive capacity and help taking decisions both in ex-ante and ex-post implementation of adaptation projects. The National Bank for Agriculture and Rural Development (NABARD) has been chosen as the National Implementation Entity (NIE) for Adaptation Fund (AF) projects in India. This necessitates NABARD provide guidance on VCA methodologies to project EEs so as to achieve uniformity in the way vulnerabilities are assessed across the portfolio of projects. There are different views on what should constitute VCAs in the context of climate change and as a result a range of methodologies representing quantitative, qualitative and combination methodologies have been employed in the literature. There are several ways in which VCA methodologies are adopted on the ground to suit the purpose and locationspecific capacity considerations. In general, VCA methodologies tend to be complicated as they try to take into consideration the vast complexities manifested on the ground. Climate vulnerabilities can also emerge from the failure of developmental interventions in considering climate change as a factor.
The National Bank for Agriculture and Rural Development has been chosen as National Implementation Entity for Adaptation Fund projects in India.
While there are many conceptual factors underpinning climate change VCAs, there are also diverse adoptions of these concepts on the ground in India and elsewhere. Taking stock of these experiences in terms of their strengths and weaknesses should be an important consideration before coming up with a VCA methodology suitable for NABARD. Developing a VCA methodology for NABARD should take into consideration the needs and necessities of both the implementing and executing organizations; it should also be suitable to different types of projects that are to be implemented and be within the available capacities of the institutions that execute the adaptation projects on the ground. 2
Keeping the above background in mind, this report attempts to review existing VCA methodologies, assess the limitations and potential advantages they could offer to NABARD, and suggest a methodology that can be used by all stakeholders engaged in implementing adaptation projects in India.
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2. THE CONTEXT 2.1 V ULNERABILITY ASSESSMENT REQUIREMENTS BY THE A DAPTATION F UND An adaptation project or program is a “Set of activities aimed at addressing the adverse impacts and risks posed by climate change.” Adaptation Fund, 2014
The Adaptation Fund (AF) was established by the Conference of the Parties (COP)2 to the United Nations Framework Convention on Climate Change (UNFCCC) in accordance with the decision 10/CP7. The decision 1/CMP.3 stipulates that the AF should assist vulnerable countries in meeting the costs of adaptation, finance adaptation projects and programs that are based on country needs and priorities and that are driven by the Parties. It has been suggested that the Parties shall design and implement projects that specifically address the particular needs of the most vulnerable communities in the eligible countries. Hence, conducting VCAs by Parties at the project and programme level constitutes an important requirement for the effective use of adaptation funds. The AF defines an adaptation project or programme as a “set of activities aimed at addressing the adverse impacts and risks posed by climate change.” It advises that the activities carried out shall be able to produce tangible results on the ground in terms of reducing the vulnerabilities and increasing adaptive capacity of communities to climate change. Here, the word tangible should be noted in particular because it determines the nature of the vulnerability assessments to be conducted for climate change adaptation. A tangible reduction in vulnerabilities entails that such reductions are substantial, measurable, definite and are able to be communicated to a variety of stakeholders engaged in adaptation. In terms of reducing vulnerabilities, the AF stresses that the vulnerabilities of specific groups should be addressed. Such groups include women, children, girls, indigenous people, tribal groups, minority groups, displaced people, refugees, people with disabilities and people with health considerations such as HIV/AIDS and other marginalized groups. The social policy of the AF also stipulates that the projects shall not exacerbate the
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Conference of Parties (COP) is the highest decision-making body of the UNFCCC. For more history on UNFCCC and how it operates, please refer to http://unfccc.int.
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inequities of these groups possibly leading to maladaptation. In addition to social considerations, the AF also requires the implementation agencies to consider natural systems for addressing vulnerabilities.
2.2 O BSERVATIONS FROM AF APPROVED PROJECTS In general, most project proposals have discussed vulnerabilities without clearly demarcating them in the organization of the text.
Several project proposals submitted and approved by the AF were reviewed to understand how these projects addressed the vulnerabilities. At the time of writing, 41 funded projects and 19 endorsed project proposals were available on the AF website.3 A review of the funded projects was carried out to understand the ways in which these projects have approached the VCAs (Figure 1). The project proposals were reviewed using the following criteria: a) nature of description of vulnerabilities (i.e. qualitative, quantitative or both); b) if the project proponents have conducted the VCAs or described the vulnerabilities based on a literature review or a combination of both; c) whether the project has proposed VCA as a part of the project activity; d) if the proposal was able to clearly identify a methodology for VCA to be carried out under the project; e) if the proposal was able to prioritize geographical and social sections based on the VCAs; and f) any other unique aspects related to VCAs such as assessing future vulnerabilities, etc. The results are presented in Figure 1 expressed as the percentage of proposals that satisfied these criteria. Important messages that emerged from the review of the project proposals are: 1. In general, most proposals have discussed the vulnerabilities. However, it was difficult to clearly identify the sections where vulnerabilities are discussed. While few proposals have included a dedicated section to describe climate change vulnerabilities to be addressed by the project, many proposals have described the vulnerabilities mixed with different sections of the proposal making it difficult to get a clear picture of prioritized climate change vulnerabilities to be addressed by the project. While this could be partly due to
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Available at https://www.adaptation-fund.org/funded_projects.
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not adhering to the guideline on project proposal layout, it also reflects the extent to which the proponents do not understand the issues nor have access to vulnerability assessment tools.
Figure 1: Evaluation of Adaptation Fund projects on addressing vulnerabilities There is a dearth 2. Some proposals lacked clear description of vulnerabilities of clear (22%, see Figure 1) and others have identified vulnerable description of groups and regions but were unable to clearly state the vulnerabilities specific vulnerability assessment methodologies to be and identification followed as part of the project activities (78%). Out of all the of specific projects, 78% of the proposals gave a clear description of vulnerability vulnerabilities, but out of these, only 19% combined the assessment qualitative and quantitative approaches. Only 19% of the methodologies in projects had conducted their own VCAs for developing the several of the proposal and 66% proposed to conduct VCAs as a part of the proposals project activity. Some proposals did not include VCAs as a reviewed.
part of the consultative framework for proposal development, while others have shown linkage with ongoing projects where the VCAs will be developed and used in AF-funded projects. Most proposals included VCAs as part of capacity building programs they propose to implement in the project. Most projects have not done VCAs when preparing the project proposals (81%) and it is fairly common to find proposals
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Most proposals could not establish clear linkage between vulnerabilities identified and adaptation interventions suggested.
that have not proposed to conduct VCAs as a part of the project activity (34%). 3. Most proposals (94%, see Figure 1) reviewed the climate change impacts and vulnerability literature, and also made efforts to discuss both the historical and projected climate change impacts. However, the VCAs did not take into consideration the possible future vulnerabilities. In this regard, it is notable to mention the proposal by Costa Rica which not only prioritized the project areas based on vulnerabilities but also differentiated current vulnerabilities from future vulnerabilities. Proposals have made efforts to link vulnerabilities identified in the NAPAs and national level policy documentation such as poverty reduction strategy papers. 4. AF put specific emphasis on identifying and addressing vulnerable groups. However, most proposals have failed to identify vulnerable groups clearly and very few proposals were able to identify and classify target regions based on VCAs, either conducted by the project or from the literature review. Most proposals have proposed to identify and target vulnerable groups and regions as a part of the project activity. There are several projects that do not identify vulnerable groups during project implementation. Only 34% of the proposals have either used some kind of VCA methodology or proposed the use of VCA methodology to prioritize the project areas (Figure 1). 5. Among the proposals that have cited to conduct VCAs or have already included preliminary VCAs (22%, Figure 1), there are those that have either used or proposed the concept of community-based participatory VCAs as opposed to technical approaches such as integrated simulation models and geographical information systems (GIS)-based approaches. However, very few proposals have used a combination of approaches wherein the vulnerability indicators were identified in a participatory manner and quantification and depiction were made using GIS tools. Even fewer projects have combined environmental and social risk management plans with the VCAs and have proposed to develop vulnerability scenarios for the future. VCAs were stronger and more thorough wherever the projects were partnered with an academic or research-based institution or an international multilateral agency. 8
6. Most project proposals lacked a clear linkage between adaptation interventions suggested and vulnerabilities identified. The rationale for identifying adaptation interventions appears largely to be up to the preferences of the stakeholders consulted during the proposal preparation process, rather than based on the concrete evidence for these interventions to address the vulnerabilities identified.
2.3 V ULNERABILITY ASSESSMENT REQUIREMENTS OF NABARD NABARD has been assigned the role of providing staffing, experience, expertise, and internal controls necessary for implementing adaptation projects in India.
NABARD Mumbai has been selected as the National Implementation Entity (NIE) by the Adaptation Fund Board (AFB) to properly manage project implementation and grant funds to implement adaptation projects in India. As NIE, NABARD has been assigned the role of overseeing overall project management including financial, and monitoring and reporting responsibilities of the projects funded through the AF4, and is responsible for submitting the project and programme proposals to AFB. This report addresses the needs recognized by NABARD as NIE to provide sufficient guidelines on VCA methodology that could be applied across the range of projects funded and implemented through NABARD. To understand the VCA methodologies that an agency like NABARD can apply, project proposals and concept notes submitted to NABARD by various Executing Entities (EEs) were reviewed to look into the nature of the vulnerabilities that these projects aim to address during the project cycle and that are presented in Table 1. Not all projects have clearly identified the vulnerabilities in a structured manner. Hence, the vulnerabilities and indicators presented in Table 1 were in a way gleaned from 4
Adaptation Fund (AF) was established to assist vulnerable countries to address adaptation needs. The fund primarily consists of funds generated by sale of 2% of the certified emission reduction (CER) credits issued under the Clean Development Mechanism (CDM). In addition, the AF also receives contributions from national governments, private sector and individuals. The AF is governed by the Adaptation Fund Board (AFB) consisting of 16 members and 16 alternates representing Parties to the Kyoto Protocol. Adaptation funds are made available to implement projects and programs through national, regional and multilateral implementing entities. In order to distribute adaptation funds to the vulnerable countries, AFB has established an accreditation procedure and guidelines where these entities are accredited to implement adaptation projects and are governed by a set of operational policies and procedures. More details on Adaptation Fund and accreditation can be found at https://www.adaptation-fund.org.
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project proposals and concept notes. The vulnerabilities are largely location-specific. However, some commonalities could be identified such as poverty, lack of alternative livelihoods, and dependency on livelihoods that are directly impacted by climate change, etc. Table 1: Nature of vulnerability elements in various project proposals submitted to NABARD Project Climate proofing of watershed development projects in the states of Tamil Nadu and Rajasthan
Vulnerabilities identified Dependency on rain-fed farming High poverty levels Soil erosion Degradation of irrigated lands Water pollution Over exploitation of forest stocks Declining water table Input intensive agriculture with mono-cropping Climate variability and projected changes
Climate smart actions and strategies in the northwestern Himalayan region for sustainable livelihoods of agriculture
Increasing extreme rainfall events with high run-off and soil erosion Receding glaciers Flash floods Significant warming Advancing cropping season Erratic rainfall Shifting apple cultivation zone Marginalized groups Deforestation Decline in water availability Steep slopes, small landholdings with low productivity and input supply Declining fodder source Loss of agro biodiversity
Enhancing adaptive capacity
Water scarcity Declining forest cover 10
Proposed activities Soil and water conservation structures Improved farming practices: deep tillage, application of tank silt, nutrient management, change of cropping patterns and integrated farming systems Agro-forestry and agrohorticulture Micro-irrigation, energy efficient devices Agro-meteorological observatory and crop insurance Site-specific vulnerability studies Training and capacity building Formation of SHGs Weather forecast and advisories Introduction of fruit crops and fodder resource development Water harvesting and storage, etc. Climate smart housing for livestock and health management, etc. Community-based conservation and seed banks, etc. Disaster preparedness Livelihood promotion for hill women (e.g. bioprospecting) Develop land and water master use plans
Project and increasing resilience of small and marginal farmers in Purulia and Bankura districts of West Bengal
Vulnerabilities identified Soil erosion due to undulated terrain Landless and unemployment Lack of access to credit Poor knowledge among communities Multi-hazards Warming low temperatures and reducing rainfall in southern region Shortening duration of rainy season (onset) Flooding of Aman rice and warming effecting winter crops Poor linkage with government and its services Poor village level institutional development Conservation and Poverty management of Women-headed poor coastal resources families as a potential Landless with limited asset adaptation base strategy for sea Lack of social services level rise Lack of essential infrastructure Overexploited fisheries Coastal dependent livelihoods Salinization of groundwater Urbanization Industrialization Environmental degradation Frequent storms Sea level rise Building adaptive Lack of market capacities of infrastructure small inland Poor access to savings, fishermen credit and insurance community for systems climate resilience Poor institutional support to and livelihood small pond fishers security, Madhya Lack of capacity of small Pradesh, India fishers to fulfill institutional and legal requirements 11
Proposed activities Timely weather-specific crop advisory services Introduce drought-tolerant varieties Soil and water conservation practices IPM practices Social forestry and sericulture Promote biogas Integrated farming system with animal husbandry and organic farming Establish climate resource and weather monitoring centers Disaster proofing with fodder and seed banks, village level plans, energy efficient stoves, water filtering, etc. Integrated mangrove fisheries farming system Restoration of degraded mangroves Training and capacity building on above Community mobilization and organization Capacity building for coastal protection and livelihoods
Technical modernization of pond design Introducing fish insurance Catchment treatment Temperature regulation of ponds Promotion of poly culture Oxygenation Water quality and nutrition
Project
Most vulnerabilities identified are rooted in the broader developmental contexts including urbanization, industrialization and social marginalization.
Vulnerabilities identified
Proposed activities management Improving the productivity of small pond fisheries Strengthening market and institutional linkages Knowledge generation and dissemination Source: Compiled from project proposals submitted by NABARD to Adaptation Fund5
The following observations emerged from Table 1 which are relevant for designing the VCA methodology. Linkage between vulnerabilities and interventions: Table 1 shows that each project on an average aims to introduce seven interventions to benefit communities in the project locations. For this the effectiveness of these interventions will have to be reflected in reducing the vulnerabilities identified (Column 2 in Table 1). One of the issues that most projects suffered is presenting no or inadequate evidence for linkages between interventions and vulnerabilities identified. In absence of such clear linkages, it will be difficult for projects to show significant vulnerability reductions impacting the ultimate adaptation effectiveness of these projects, thus further diminishing the potential for scaling up. Broad range of vulnerabilities: Though the projects are principally centered on natural resource management, including agriculture and forestry, the identified vulnerabilities are in fact rooted in broader developmental contexts, including urbanization, industrialization and social marginalization. Vulnerability assessment methodologies should be able to capture some of these broader dimensions as they could be exacerbated by climate change and the associated resource crunch. Natural resource management and livelihood centered: Though the proposals showed a broad range of vulnerabilities, the project interventions are focused primarily on natural resource management that aim to strengthen livelihoods and income security. Despite this focus, the projects should be able
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These projects can be accessed from https://www.adaptationfund.org/funded_projects.
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to reduce overall vulnerabilities by targeting the linkages between vulnerability factors associated with natural resources management and those associated livelihoods and vulnerability factors emanating from outside natural resource management. Nature of interventions: Most projects relied heavily on technical interventions (e.g. water harvesting, afforestation and different natural resource management practices) and to a very limited extent on social and institutional strengthening. As a result, most of the capacity building programs were designed and are centered on building capacities to implement these technical interventions. While projects are mainly orientated towards agriculture technology, they also integrate components such as rural development, rural energy, social capital, land management, etc., which will have overarching impacts. As a result, vulnerability assessments should be able to capture the efficacy of these interventions in reducing the underlying vulnerabilities of communities that these projects aim to address.
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3. UNDERSTANDING CLIMATE CHANGE VULNERABILITY 3.1 T HE CONCEPT OF CLIMATE CHANGE VULNERABILITY “Vulnerability is the degree to which human and environmental systems are likely to experience harm due to perturbation or stress.” Luers et al., 2003
Vulnerability, resilience and adaptive capacity are three fundamental concepts that cannot be over-emphasized in CCA. Good understanding of vulnerability and resilience is crucial to the development of sustainable adaptation strategies (Harley et al., 2008).6 The concepts of vulnerability took shape and gained greater attention among policy makers and development practitioners largely due to the work that was carried out in the field of hazards and disaster risk reduction (DRR). However, vulnerability concepts have also been widely applied in other fields, including sustainable development (SD), health, poverty reduction and environmental management. Several definitions of vulnerability have been put forth by both CCA and DRR scholars. In general, vulnerability has been defined as “the degree to which human and environmental systems are likely to experience harm due to perturbation or stress” (Luers et al., 2003). According to IPCC (Schneider et al., 2007), climate change vulnerability is “the degree to which geophysical, biological and socio-economic systems are susceptible to, and unable to cope with, adverse impacts of climate change.” According to UNISDR, vulnerability is defined as “the characteristics and circumstances of a community, system or asset that make it susceptible to the damaging effects of a hazard (UNISDR, 2015).” UNISDR further underlines that vulnerability can originate from a range of physical, social, economic and environmental factors. It is often regarded as the characteristics of the system of interest (i.e. community or society or asset), and is independent of the exposure that the system is subjected to.
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The definitions to the glossary of terms are provided in Annexure 2.
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Vulnerability is the function of exposure, sensitivity and adaptive capacity.
Vulnerability can be understood as a concept, as a state of a system and as a process (Prowse, 2003). Vulnerability can be seen as a dynamic concept as it recognizes and captures changes happening around the system in question. As a state, vulnerability should be understood as the condition that predisposes a particular system to be affected by hazards. Vulnerabilities could also emerge due to processes operating within and beyond the vicinity of a society, but not necessarily caused by the society itself. For example, a village situated near a mine is vulnerable to a variety of impacts due to the mining activities happening in its vicinity even though the villagers may not engage in mining directly. The pollution and other health impacts of mining could predispose the villagers to the impacts of an impending natural hazard. Hence, vulnerability factors could be intrinsic to the system as well as exogenous to the system that constantly tests the system’s ability to withstand external pressures. Vulnerability can manifest in the form of economic, social, institutional and natural (biological, biophysical and environmental) systems with which communities interact regularly. According to IPCC, vulnerability is the function of exposure, sensitivity and adaptive capacity. Exposure refers to the natural, physical and other elements to which people and infrastructure are exposed to potential losses upon occurrence of a hazard. Here, the concept of exposure is constructed as the severity with which the physical, social and natural elements are subjected to in terms of magnitude, duration and frequency of natural disasters. Exposure differs from hazard to hazard. For example, droughts normally do not cause physical damage to infrastructure while floods and typhoons do. For quantification purposes, exposure can be considered as all the elements— physical, social and natural—present in an area where a hazard may occur irrespective of their socio-economic and physical condition. Sensitivity is defined as “the degree to which a system will respond to a given change in climate, including beneficial and harmful effects” (McCarthy et al., 2001). Sensitivity is the major factor that determines the consequences of exposure to a natural hazard. Factors that determine the system’s sensitivity are those that predispose the system to the losses from the hazard. For example, a household in a low-lying area, in a dwelling with low 16
Adaptive capacity is a denominator to sensitivity and works against factors that predisposes communities to impacts.
elevation and poor disaster preparedness, will be severely impacted by floods compared to a household living in the same locality, but in an elevated dwelling with adequate preparation. Hence, all households living on a flood plain are not equally impacted by flooding, rather the impacts differ according to the socio-economic conditions that define their predisposition to the hazard impacts. However, exposure is a necessary but not a sufficient condition of disaster impacts (Cardona et al., 2012). Communities living in low-lying areas can be said to have high sensitivity to floods compared to those living in elevated areas. Here, predisposing factors include low lying areas, poor transportation and lack of disaster preparedness. Several other factors, including poverty, governance levels, livelihoods that are directly impacted by weather and climate factors, can be considered rendering communities sensitive to the impacts of disasters. Sensitivities do not translate into impacts unless a flood event happens. Hence, vulnerabilities are realized only when hazards meet sensitivities. As a result, sensitivities and hence vulnerabilities, can be masked for several years until a hazard occurs and takes the communities and other actors by surprise. This is where conducting regular vulnerability assessments could help to unearth ‘hidden vulnerabilities’ before hazards occur so that both preparedness and mitigation measures could be taken up to deal with the potential impacts. From this point of view, it is important to factor in climate change when looking at understanding and estimating vulnerabilities. There also needs to be information on how hazards, sensitivities and capacities change as a result of climate change and associated variability. Adaptive capacity refers to the ability of an entity to negate the negative impacts of a disaster, including the ability to utilize the opportunity to harness the advantages that climate change may provide. Adaptive capacity can be considered a denominator to sensitivity and may work against the sensitivity factors that communities and other entities have. For example, factors such as the presence of strong social bonding, protective natural vegetation and strong leadership can work as capacity to reduce the overall impact of floods on a range of time scales. Not all capacities can be mobilized at the same time; certain capacities can be accessed right away while others take time to mobilize. 17
The objective of adaptation is to move from a vulnerable to a resilient situation leading to continuous increments in wellbeing.
The closer the capacities are to the communities, both in terms of geographical and time proximity, the sooner the communities will be able to utilize them and mitigate the potential impacts. Figure 2 depicts the relationship between vulnerability (as determined by the exposure and sensitivity components), adaptive capacity and net impacts. What communities undergo after a climatic event such as flood or drought is a result of the combination of sensitivity and adaptive capacity. Hence, adaptive capacity plays a vital role in buffering the shocks. Designing adaptation interventions should not only be based on assessing the sensitivities but also on assessment of capacities that exist within communities and those that could be readily mobilized within a short span of time from outside. For this reason, the methodology to be developed for use by NABARD would also have to take into consideration the existing capacities.
Figure 2: Relation between vulnerability, adaptive capacity and net impacts (Prabhakar, 2013)
When addressing the issue of climate change adaptation, the idea of vulnerability comes into the forefront of discussion.
Vulnerability can also arise from loss of resilience in a system due to the dynamic nature of natural hazards associated with climatic variability and change, and also due to socio-political drivers associated with the system. In other words, increasing the resilience of a system can have positive benefits in terms of vulnerability reduction. Hence, it is important to factor in indicators of resilience in vulnerability assessments. Resilience is about people’s capacity beyond the minimum of being able to cope. A resilient community is able to bounce back or return from a shock and remain unchanged. Adaptation in the context of climate change is an effort or action towards reducing the negative impacts of climate change (Keithley and Bleier, 2008). The concept of adaptation cannot be 18
conceived by designing and implementing projects alone, as it is also about reducing vulnerability and building resilience. As a matter of fact, when addressing the issue of climate change adaptation, the idea of vulnerability comes into the forefront of discussion (SPREP, 2009). The actual impacts of climate change can be reduced by: (1) promoting resilience so as to reduce system sensitivities; (2) increasing adaptation capacity and effectiveness of adaptation responses; and (3) improving the adaptation-planning processes (Grafton, 2009). The graphs below illustrate vulnerability, resilience and adaptation situations (Adapted from Ilori and Prabhakar, 2014). The graphs are simple examples of vulnerability, resilience and adaptation within the context of climate change (Figure 3). A drought occurrence could affect the wellbeing of a poor household or community at large. In a vulnerable situation, the community may not be able to return to its original wellbeing level (graph 1 in Figure 3). The first graph in Figure 3 is a typical example of a vulnerable household or community that is prone to the risk of climate change. Any perturbation in the climate system would lead to a decline in overall wellbeing.
Figure 3: From vulnerable situation to resilient and adaptation situation Source: Adapted from Ilori and Prabhakar, 2014 A resilient community will be able to bounce back from a climate disaster (graph 2 in Figure 3) and the degree of resilience can determine the speed with which a household or community can return to the original wellbeing level. Resilience would be an important asset as the climate changes. An occurrence of 19
drought only leads to a temporary decline. The system is able to adjust after some time and return to normal. The third graph is typical of a household or community that has moved beyond resilience to being able to adapt fully to a new climate. Wellbeing does not change over the course of the drought. They are fully adapted to the drought perhaps through the use of drought-resistant crops for farming or through an early warning system that would alert them that drought is coming so they can prepare for it. This typifies an example of anticipatory adaptation. Vulnerable communities impacted by climate change will not be able to go back to their previous condition without external intervention. The objective of any adaptation intervention is to move from a vulnerable situation to a resilient or adaptation situation.
3.2 V ULNERABILITIES AS C OMMON D ENOMINATORS
Vulnerabilities work as common denominators for CCA, DRR and SD, and hence addressing vulnerabilities has to be the first step for achieving any of the outcomes of CCA, DRR and SD.
From the above discussion, it is evident that the impacts from natural disasters, which originate either from long-term climate change or from climate variability, are due to the underlying vulnerabilities of individuals, societies, regions and nations. In a way, most vulnerabilities work as common denominators for CCA and DRR and hence for SD. For this reason, addressing underlying vulnerabilities has to be the first step for achieving any of the outcomes of CCA, DRR and SD. It is important here for the reader to understand the synergies between CCA, DRR and SD and how certain vulnerabilities can affect these outcomes. CCA, DRR and SD are complementary fields. In order for CCA interventions to be successfully implemented, it is now widely understood that they should be included as part of SD programs. Also, it would be injudicious to implement future SD programs without taking CCA and DRR into consideration (IPCC, 2007). The Intergovernmental Panel on Climate Change (IPCC) defines CCA as ‘adjustments in ecological, social, or economic systems in response to actual or expected climatic stimuli and their effects or impacts. It refers to changes in processes, practices and structures to moderate potential damages or to benefit from 20
Both climate change adaptation and disaster risk reduction address the underlying causes of vulnerability.
opportunities associated with climate change’ (IPCC, 2007). DRR has been described as ‘the process of reducing exposure, lessening underlying vulnerabilities, better management of resources and improved preparedness towards future hazards’ (Setiyadi et al., 2010) and is clearly relevant to CCA. From these definitions, it can be seen that both CCA and DRR address the underlying causes of vulnerability to a hazard or risk. In addition to shocks, CCA also addresses the need for long-term adjustment to slow onset changes. Figure 4 presents how DRR, CCA and SD fall on time and complexity scales (Klein, 2002). Both CCA and DRR are comparable in terms of complexity but the outcomes of DRR could be achieved in the short term compared to the CCA outcomes. In comparison, SD has been regarded as an aspirational goal which spans from short to long term and is complex in nature both from the perspective of understanding and achieving it as an outcome.
Figure 4: Synergies between CCA, DRR and SD Often, measures adopted for CCA are aligned with those used in the DRR field (Prabhakar et al., 2015). The key difference between these two approaches is that in the case of DRR historic data is analyzed, whereas for CCA more emphasis is placed on future predictions (Economic and Social Commission for Asia and the Pacific Committee on Disaster Risk Reduction, 2013). SD requires analysis of socio-economic, political and demographic issues as underlying causes of vulnerability; these issues are also fundamental to adaptive capacity. SD thus reduces vulnerability, and as a result, resilience and adaptive capacity are strengthened (for definitions of resilience and 21
adaptive capacity, refer to Annexure 2). Achieving SD requires development initiatives to be realigned with CCA and DRR concerns. Building resilience in communities has been found to be an effective way to reduce disaster risk as well as vulnerabilities (World Food Program, 2011). Hence, the concept of resilience may offer a means of breaking down the individual concepts of DRR and CCA, thus presenting a common crosscutting theme (United Nations, 2012). Some authors suggest that SD itself could serve as a means of adaptation as it is directly linked with vulnerability reduction (Suarez and Ribot, 2003).
3.3 V ULNERABILITY THRESHOLDS A stress that can overpower capacities can lead to net negative impacts suggesting a possible critical threshold of capacities.
Climate change stress that overpowers existing capacities can lead to negative impacts. This suggests the possible existence of a critical threshold for capacities and vulnerabilities that could tip communities either into perpetual loss or gain of wellbeing. Successful adaptation should lead to reduced vulnerability to climate stresses (Srinivasan and Prabhakar, 2009). Therefore, the effectiveness of adaptation may be considered as a function of changes in vulnerability over time. This provides a theoretical basis that the measure of vulnerability should be related to the determinants of vulnerability – exposure (climatic stimuli that a system experiences), sensitivity (responsiveness of a system to climate stimuli), and adaptive capacity (potential ability of a system to make adjustments to climate change) (Brooks, 2003; Brooks et al., 2005; Hobday et al., 2006; Luers et al., 2003; Rosenzweig and Tubiello, 2006; Smit et al., 2000; Yohe and Tol, 2002). Each of these determinants of vulnerability in turn may be represented by several indicators. A system that is endowed with all key determinants might show higher resilience and adaptive capacity with higher probability of net low impacts. On the other hand, a system that is not endowed with such factors may be adversely impacted by climatic stimuli thereby increasing its vulnerability to further climatic stresses and leading to a vicious cycle. This theory is depicted in Figure 5. However, there is no evidence suggesting the means to assess the critical thresholds. It remains a concept that should be kept in mind while assessing and addressing vulnerabilities. It should be noted that in the VCA methodology developed for NABARD, the thresholds used are not critical thresholds but a range of 22
values within which values of a particular vulnerability indicator falls, helping to normalize the indicators for deriving the index.
Figure 5: Relationship between vulnerability, determinants of adaptive capacity and impacts (Prabhakar and Srinivasan, 2010)
3.4 V ULNERABILITY ASSESSMENT METHODOLOGIES AND TOOLS I NDICATOR - BASED
APPROACH TO ASSESS
VULNERABILITIES Quantifying vulnerability indicators has been an important and common approach among various VCA methodologies currently being employed.
As will be clearly seen in the subsequent section on comparing VCA methodologies, identifying and quantifying vulnerability indicators have assumed an importance among various VCA methodologies being proposed and among those that are being adopted. This is the reason that indicators provide an easy way to grasp different components engaged in VCA and show how they relate to each other in the final outcome of the VCA. Most developmental assessments including the Human Development Index and those carried out by multi-lateral developmental agencies such as the World Bank, Asian Development Bank and UN agencies employ indicators to track progress in developmental activities. Familiarity with sources of data can be 23
Building resilience in communities has been found to be an effective way to reduce disaster risks and vulnerabilities.
obtained from publicly available sources for most generic indicators such as census records and sample surveys. The proposed methodology for NABARD also provides certain flexibility so that certain types of indicators for which the data are not available can be obtained through measurements or estimations or by using proxy indicators. The need for using indicators in VCAs is supported by scholars such as Vincent and Cull (2014) who stated that “In social, or context vulnerability, vulnerability is a potential state that determines whether hazard exposure will translate into adverse impacts. It is therefore necessary to rely on indicators that best represent the complex underlying processes.” Studies employing indicators identified them either through an inductive or deductive approach. With an inductive approach the vulnerability indicators are selected from a wide variety of indicators. With a deductive approach the indicators are often chosen based on a theoretical framework that is constructed to explain the underlying vulnerabilities. Inductive approaches are often intensive and data-driven. The final identification of indicators can be done either through expert judgment or multicriteria analysis. It is not uncommon that various indicators are combined to form indices. Several international indicators developed by the World Bank, UNDP and IDB for the assessment of disaster risk may be useful as metrics for monitoring the progress of adaptation to climate change. For example, the Disaster Risk Index (DRI) developed by the UNDP and the Hotspots index developed by the World Bank are deductive7 and are built upon theoretical constructions of vulnerability which are then populated with existing secondary data. They both aim to show human vulnerability to disaster risk. Changing levels of such vulnerability over time might indicate the implementation and/or effectiveness of adaptation actions. The DRI and Hotspots indicators have national and sub-national resolutions, respectively, and both of them calibrate indexes for vulnerability against disaster loss. This adds rigor but it also means the measures are retrospective in nature and are dependent on the quality of externally-derived
7
Deductive refers to a reasoning scenario where in the observations from the real world are explained based on a single or a set of theories. It differs from inductive approach where in the generalizations are drawn based on a set of observations made.
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input data. The key advantage of these approaches is that they are centrally-managed providing costs and quality control and are easily repeatable over time and space (Pelling, 2008). The key limitations are that the models follow theory not data, and the resolution, coverage and quality of data and output are externally set. They are also tied to historic and snapshot visions of current vulnerability but not future vulnerability.
I DENTIFYING
INDICATORS BY UNDERSTANDING IMPACT
PATHWAYS
Figure 6 shows the path along which climate change impacts the local agriculture economy and subsequently the larger economy, which could provide a valuable basis for identifying and narrowing down specific vulnerability indicators. The figure shows how vulnerabilities along the line of the impact pathway would exacerbate climate change impacts.
Figure 6: Climate change impact pathway for agriculture and macro-economic linkages (Prabhakar and Srinivasan, 2010) The concept of an impact pathway provides us with the following understanding that helps in identifying vulnerability indicators: a. Addressing all kinds of vulnerabilities assumes importance for the reason that environmental or biophysical vulnerabilities could lead to individual vulnerability. This is especially true in the case of those who depend on natural resources for their livelihoods, which is a common occurrence in agriculture and other forms of rural-based livelihood groups. b. Similar to how vulnerability of natural resources leads to individual vulnerabilities, individual vulnerabilities can result in collective or social vulnerability. Hence, indicators
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representing both individual and the natural resource base have to be taken into consideration. c. The VCAs should have relevance to the same geographical boundaries and contexts in which adaptation projects are implemented due to pathways through which climate change impacts various elements of agriculture and dependent livelihoods. d. Due to the presence of individual and societal vulnerabilities, community-based approaches, especially utilizing participatory rural appraisal techniques, can be useful in obtaining firsthand information on societal and individual vulnerabilities. e. There is a greater degree of chance that individual and societal vulnerabilities could differ from location to location due to varying types of interactions between environmental and societal factors.
A VAILABLE Most of the VCA methodologies have utilized participatory approaches involving communities at the local level.
VULNERABILITY ASSESSMENT
A PPROACHES
The vulnerability concepts discussed earlier in this section have been applied differently in different contexts leading to several applied forms of VCA methodologies. Error! Reference source not found. provides a comparison of different VCA methodologies developed by various international organizations. The VCA methodologies employed in the Indian context are listed in Error! Reference source not found. along with their pros and cons. A perusal of the methodologies provides us with the following conclusions: 1. All the methodologies, irrespective of whether qualitative or quantitative, have used the exposure, sensitivity and capacity model for VCA. 2. Most VCA methodologies have utilized participatory approaches involving communities at the local level. A variety of tools were employed within participatory approaches. For example, for assessing exposure, tools such as seasonal calendar, historical timelines and rain calendars were used. For assessing sensitivities, tools used were hazard mapping, mental models, hazard trend analysis, ranking and hazard impact on livelihood matrix. For assessing adaptive capacity, the methodologies have used tools such as social maps, resource maps, Venn diagrams, preference ranking, 26
Table 2: Climate change vulnerability and risk assessment parameters considered by different organizations Vulnerability
Biophysical impacts
Livelihood impacts
Hazard prioritization
Coping strategies
Livelihood assets Community awareness/knowled ge Capacity to plan and effect change
0
0
0
0
0
0
0
0 0
0
0
Current climate trends Climate-induced events
Current hazard trends
Adaptive Capacity
Community based and scientific data
Sensitivity
Climate projections
A framework for social adaptation to climate change, IUCN Climate vulnerability and capacity analysis, Care CVAAA, SPREP & CIDA Vulnerability to resilience, Practical Action Participatory tools for assessing climate change impacts and exploring adaptation options, LFP & UKAID Adaptation toolkit, Christian Aid CRiSTAL, IISD CEDRA, Tearfund CBA, IIED
Exposure
Vulnerability = f(E,S,AC)
Framework
Note: means applicable, 0 means not applicable, - means did not fully accommodate Source: Adapted from Practical Action et al., 2010 27
Table 3: Comparison of various vulnerability assessment approaches in the Indian context 8 Approach Participatory capacity and vulnerability assessment in West Bengal, India
Climate proofing watershed programs
Application Diversifying agriculture livelihoods through integrated agriculture production systems Watershed development programs in Dindigul district of Tamil Nadu
Pros Based on exposure, sensitivity and capacity model Participatory approach leading to greater engagement of stakeholders and ownership of the project Employing different participatory rural appraisal (PRA) techniques such as group discussions, crop and climate calendars, historical analysis, etc. Based on exposure, sensitivity and capacity model Detailed historical analysis of rainfall and temperature trends in the watershed and projection of climate change using Soil and Water Assessment Tool (SWAT) Structured questionnaire surveys to identify historical trends, adaptation options and capacities Climate proofing table forms the key tool for linking vulnerabilities and interventions Participation of communities through structured questionnaire surveys
8
Cons Qualitative approach limiting the potential for cross-site comparison of degree of vulnerability; no evidence of vulnerabilities being quantified
Reference GIZ, 2013
Vulnerabilities are not quantified Least focus on the social vulnerability assessment. especially identification of sensitivity factors and interaction between social with biophysical vulnerabilities
GIZ, 2012
Methodologies were assessed from the literature available on the web and, hence, comparisons are limited to the nature and details of information available on each methodology employed.
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Approach Climate proofing of development
Application Generic framework developed that can be applied to a variety of contexts
Climate change vulnerability assessment in Gangetic basin based in Hahn et al. 2009 Livelihood Vulnerability Index
Vulnerability assessment of people, livelihoods and ecosystems
Pros Based on exposure, sensitivity and capacity model Comprehensive and can be applied at various scales Clear step-wise instructions and covers both biophysical and socio-economic impacts Process is key principle giving high priority to participatory techniques
Based on exposure, sensitivity and capacity model Quantification of vulnerabilities in the form of index that enabled precise comparison of vulnerabilities between locations Use of index proved easy to communicate and document in terms of maps for easy understanding Simple quantification and indexation
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Cons Very generic requiring more clear examples how the methodology can be adopted to different location-specific conditions No evidence of vulnerabilities being assessed in quantitative manner; not clear if it provides comparability of results across locations and time scales and, hence, difficult to judge its efficacy in monitoring and evaluation Not based on participatory processes Very limited number of indicators, not capturing all the complexities involved in climate change vulnerability, though the method claims to cover people, livelihoods and ecosystems No clear rationale on how indicators identified and why certain indicators were chosen against others In exposure calculations, both demographic and climatic exposures were done
Reference Frode and Hahn, 2010
WWF,2011
Approach
Rapid assessment of rural vulnerability in Sikkim, Himalaya
Application
Pros
Vulnerability assessment of rural communities dependent on natural resources for livelihoods
Assessing climate change vulnerability using GIS
A vulnerability assessment methodology used in insurance-based projects
Cons Reference separately making it difficult to understand how they interact with each other Based on exposure, sensitivity and Suffers from most limitations Tambe et al., 2011 capacity model of the above approaches due to implementation-related Quantitative methodology (similar to aspects such as given below: that of the one developed by Hahn et Very limited number of al., 2009) indicators Expression of vulnerabilities in the No clear explanation of form of an index providing crosswhy certain indicators location comparability included against others Simple quantification and indexation Not clear how different techniques levels of vulnerability relate Employed PRA technique for obtaining to how different adaptation ground level information measures are to be identified and implemented Sharma, Based on exposure, sensitivity and Limited number of capacity model indicators used with little or 2012 no justification for the type Quantitative methodology using GIS to of indicators employed for show results in a map interface assessing vulnerability and capacity Source: Compiled by author from different sources indicated in the table
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3.
4.
5.
6.
7.
wealth ranking, communication maps, and vulnerability and capacity matrix (refer to Annexure 7 for a brief explanation on these methodologies). However, these tools are applied to a varying degree by the different methodologies listed in the tables depending on the expertise of the authors and location-specific conditions. Though most VCAs follow the exposure, sensitivity and capacity model, some VCAs are qualitative and, hence, it is difficult to understand how vulnerabilities at one location compare with those at another in terms of severity. Lack of simple quantitative methods makes it difficult to prioritize the nature and severity of vulnerabilities. Most methodologies tend to identify vulnerabilities through indicators, which are often only identified, but not often quantified. Although most methodologies have tried to identify both biophysical and socio-economic vulnerability indicators, the distinction and nature of use of these two forms of vulnerabilities are not clear. Most methodologies suffered from weak linkage between identification of vulnerabilities, quantification, and use of this information to identify CCA options. Hence, identification of adaptation options appears to be isolated, both in terms of process and linkage with the vulnerabilities identified, from the rest of the methodology and outcomes of VCA. Indicators used for exposure were mostly limited to general climatic parameters such as temperature and precipitation, where some methodologies did not identify change as an indicator. There is a wide variation in the nature of indicators representing sensitivity and capacity. Most indicators employed in these were restricted to broad indicators such as demographics and socio-economic factors such as income and education levels, which can be obtained from the village level and other census data. Most climate change VCA methodologies are aware of the need to take into consideration future projected climate change impacts in carrying out VCAs. However, practitioners are also cautioned about the overemphasis on projectionsbased VCAs. The recently released report on VCAs by the Institute for Social and Economic Transformation (ISET), part of the Mekong Building Climate Resilience in Asian Cities (M-BRACE) program, has opined that the 31
overemphasis on future climate projections could potentially make interventions narrowly-based. Such a narrow approach may have a higher chance of failure due to the uncertainty associated with future climate projections. Instead, the adaptation community was advised to focus on processes on the ground, recognizing local capacities by employing a participatory approach that could involve multiple stakeholders. With this as starting point, the inclusion of future projections could be introduced later as an increasing number of stakeholders understand the language of climate projections and involved uncertainties (Institute for Social and Environmental Transition, 2014).
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4. THE VULNERABILITY AND C APACITY ASSESSMENT INDEX (VCAI) Note: Please use the Excel-based Vulnerability and Capacity Assessment Index for Climate Change Adaptation of Natural Resource-based Communities (VCAI) tool that accompanies this document, while reading this section.
4.1 V ULNERABILITY A SSESSMENT R EQUIREMENTS OF NABARD The VCAI has been developed as an Excelbased tool using the exposure, sensitivity and capacity model.
From the preceding discussion on current vulnerability methodologies, as well as consultations with NABARD staff, it became evident that the methodology to be adopted for the purpose of NABARD-implemented projects should have the following characteristics: 1. Vulnerability is assessed using the exposure, sensitivity and capacity model, as it has been proven to be the most common approach, while providing needed robustness and flexibility. 2. The selected methodology should follow a quantitative approach, wherein stakeholders should be able to quantify the indicators and express the vulnerabilities in the form of an index. 3. Due to location specificity of certain indicators and issues with data availability, the methodology should provide an option for users to choose indicators (including proxy indicators) among a broad range collected from published literature and consultations. Project EEs should be able to choose a sub-set of indicators after prioritizing them through conducting participatory rural appraisal exercises at the project sites.
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4. Due to the quantitative nature of the methodology, stakeholders should be able to compare vulnerabilities across various sites within the project. 5. Users should be able to conduct computations in a simple to use Excel worksheet. Keeping the above requirements of NABARD in view, an Excel based tool called Vulnerability and Capacity Assessment Index (VCAI) for Climate Change Adaptation of Natural Resourcebased Communities was developed.
4.2 T HE P ROCESS The VCA methodology developed for NABARD has utilized extensive consultations with various stakeholders engaged in climate change adaptation.
The VCA methodology for NABARD was developed using the process depicted in Figure 7. The first step in developing the tool was an extensive consultation with the staff of the Farm Sector Policy Department, NABARD, who are assigned with the task of implementing Adaptation Fund projects in India. The consultation consisted of direct discussions, regular email and Skype communications, and comments on the text. The objective of the consultation was to understand the priorities of NABARD as an AF NIE, the nature of the projects submitted to the Adaptation Fund, and projects that have been implemented by various EEs associated with NABARD. Subsequent to the initial consultation, the literature was reviewed to distill lessons from existing VCA methodologies, and a suitable framework for the methodology was developed for consideration by NABARD. The methodology was developed keeping in view the requirements of NABARD that were previously outlined in this section. The literature review suggests that an indicator-based approach is the most appropriate means of assessing vulnerabilities (please refer to the sub-section on Vulnerability Assessment Methodologies and Tools). The literature review presents us with a variety of indicators that needed to be vetted for their appropriateness for inclusion in the VCA methodology for NABARD. For this, these indicators were first vetted at the community level through conducting a participatory rural appraisal (PRA) exercise as presented in Annexure 3. Subsequently, the ‘Vulnerability and Capacity Assessment Index (VCAI) for Climate Change Adaptation of Natural Resource-based Communities’ tool was developed in the form of an Excel worksheet and shared with various 34
stakeholders to obtain feedback. At the final stage, the methodology was taken through an internal consultation with extended NABARD staff representing different sections. The consultations discussed the appropriateness of the approach, focus, type of indicators included, and their organization.
Figure 7: Sequence of steps involved in developing the vulnerability assessment methodology for use by NABARD
4.3 O BJECTIVES OF THE VCAI T OOL The VCAI tool was developed with the following objectives:
Provide a common methodological framework for all EEs engaged with NABARD in implementing adaptation projects in India Help assess the exposure, sensitivity and capacity of the natural environment, communities, institutions and policies Help EEs in assessing progress in project effectiveness in terms of vulnerability reduction by comparing subsequent vulnerability assessments and different areas within the project boundary Help EEs report vulnerability assessment outputs to NABARD, which will in turn will use these results to monitor and evaluate adaptation interventions. 35
4.4 M ETHODOLOGY FOR C OMPUTING THE VCAI The Vulnerability and Capacity Assessment Index, VCAI, is computed as: Vulnerability Index (VCAI) = (E+S)-C …………………………………………………………Equation 1 Where : E is exposure value obtained by averaging the normalized exposure indicators S is the sensitivity value obtained by averaging the normalized sensitivity indicators C is the capacity value obtained by averaging the normalized capacity indicators. VCAI is computed using several sets of indicators for quantifying exposure, sensitivity and capacity components of the index. The relevant indicators are presented in Tables 4 and 5. Note: The indicators presented in the VCAI tool are not expected to be modified by the user, but rather users are suggested to prioritize and quantify them using the PRA techniques presented in the Annexure 7. However, a mechanism has been provided to suggest additional indicators in the Overview sheet of the VCAI tool (please see the subsequent section on ‘Guidelines for Using VCAI’). Since indicators are measured using different scales with values that fall in different ranges, combining these indicators into an index requires them to be brought into a unit-less value. This was achieved by normalizing these values using a linear normalization technique. The formula for normalizing the indicator values is given as:
Normalized indicator value
zi
xi Tmin ( x) Tmax ( x) Tmin ( x)
………………………………………………..…………Equation 2 Where: xi is the value of the indicator Tmin is the minimum threshold value of the indicator xi 36
Tmax is the maximum threshold value of the indicator xi. Within the tool, the Exposure (E) is calculated as the average of the normalized exposure indicators prioritized and measured by the user. Exposure (E) =
(∑𝑛 1 𝑖𝑛𝑒 ) 𝑛
…………………………………………………………Equation 3 Where: n is the number of exposure indicators chosen for the vulnerability assessment ine is the normalized value of the exposure indicator. Sensitivity (S) and Capacity (C) are calculated by using a set of indicators organized into the following six components: a) Social, institutional and policy dimensions; b) Agriculture and food; c) Water and sanitation; d) Land and infrastructure; e) Fisheries and animal husbandry; and f) Biodiversity and ecosystems including forests. The computation of Sensitivity within the tool comprises taking the average of the normalized indicator values included in these six components. A similar methodology was followed for computing Capacity within the index. Within the tool, the sensitivity (S) is computed as: Sensitivity (S) =
𝑆𝑠 +𝑆𝑎 +𝑆𝑤 +𝑆𝑙 +𝑆𝑓 +𝑆𝑏 …𝑆𝑛 𝑛𝑎
………………………………………………..…………Equation 4 Where: S is the overall sensitivity Ss= sensitivity within the component of Social, institutional and policy dimensions Sa = sensitivity within the component of Agriculture and food Sw = sensitivity within the component of Water and sanitation Sl = sensitivity within the component of Land and infrastructure Sf = sensitivity within the component of Fisheries and animal husbandry 37
Sb = sensitivity within the component of Biodiversity and ecosystems including forests na = total number of components for which sensitivity is assessed. Ss =
(∑𝑛 1 𝑖𝑛𝑠 ) 𝑛𝑖
………………………………………………………….Equation 5 Where: ni is the number of sensitivity indicators chosen for the vulnerability assessment within the component ins is the normalized value of the sensitivity indicator. A similar approach was used for computing the Capacity (C) element of the VCAI. More guidelines are provided in the following section for using the VCAI.
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Table 4: List of physical exposures included in the VCAI tool Physical exposures Air quality (air quality index, peak value in a year) Climate variability, max temperature (coefficient of variation in the past 30 years, %) Climate variability, min temperature (coefficient variation in the last 30 years, %) Climate variability, rainfall (coefficient of variation in the past 30 years, %) Delay in onset of monsoons (no. of days from the normal onset day, average of the past 30 years) Duration of cyclones (average length per event in the past 30 years, days) Duration of droughts (average length per event in the past 30 years, days) Duration of floods (average length per event in the past 30 years, days) Frost during critical stages of crop (no. of frost days) Intensity of cyclones (peak wind speed in km/h) Intensity of droughts (% reduction in RF from a 30-year average) Intensity of floods (height of flood in m) Land with elevation less than 10 m above sea-level (% of total land area) Number of cold days above threshold (% of total days in a year) Number of warm days above threshold (% of total days in a year) Projected % of land to be inundated due to sea level rise (% of total land area) Projected bioclimatic variability (% change in degree days) Projected change in precipitation (% deviation from 30-year average) Projected change in river flow (% change from 30-year average) Projected change in runoff (% of change from 30-year average) Projected change in temperature (% change from 30-year average) Projected change in water temperature (% deviation from 30-year average) Sea level rise from a baseline (cm) Untimely rainfall during critical stages of crop (no. of such rainy days)
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Table 5: Sensitivity and capacity indicators for different sectors used in the VCAI tool Sensitivity Indicators Social, institutional and policy dimensions Diseases (vector-borne, water-borne, etc.) (% affected of total population per annum) Disruption of public services during disasters (communities that received services, % of normal time)
Distance travelled by women for collecting water (km per day)
Estimated impact of future climate change on deaths from disease (projected deaths, % of population) % of informal settlements in disaster-prone areas
Failure of communication facilities during disasters (% of affected receiving early warning or other forms of communication) % of population living in kaccha and semi-pucca houses (huts or thatched houses) Female recipients of post-disaster recovery funds and material assistance (% of total recipients) Health expenditure derived from loans (% of total health expenses) Illiteracy rate (% of total population) Inequality (economic and social) (% of population that have access to financial services offered by the government) Maternal mortality (% of women population)
Mortality due to communicable (infectious) diseases (% of population
Capacity Indicators
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% of population living in pucca houses (houses using concrete and steel) % of women in high-level decision-making roles related to climate change adaptation (e.g. in the Ministry of Environment, Forestry and Climate Change, etc.) % of women involved in climate change adaptation planning (e.g. disaster risk reduction, state adaptation plans, local disaster prevention planning, etc.) Access to CCA technologies (presence of CCA technologies= 1, absence = 0) Access to communication (no. of phones per 1,000 people) Access to credit (people with deposit or loan accounts, % of total population) Access to education (no. of schools per 1,000 children) Access to energy by gender (% of female-headed households with access to energy) Access to health (no. of hospitals per 1,000 people) Access to markets (marketing costs, % of total revenue) Access to natural resources (land, forests, water, etc.) (% of people with land tenure) Alternative employment/livelihood opportunities that are not directly impacted by climate (no. of alternative livelihoods available) Availability of development plans, scenarios and
Sensitivity Indicators
affected every year) Mortality outcomes by gender in recent disasters, drought, monsoon, etc. (% deaths by gender) Number of women and men living in floodplains, low-elevated coastal zones (% of population) Policies that enhance inequality (0= none; 1= present)
Population below poverty line (% of total population)
Population density (no. per sq. km)
Population living in rural areas (% of total population)
Prevalence of respiratory diseases (% of population affected)
Proportion of children (% of total population) Proportion of elderly (% of total population) Proportion of indigenous groups (% of total population) Proportion of pregnant women (% of total population)
Proportion of underweight children (% of total child population)
Proportion of women-headed households (% of total households)
Rate of anemia prevalence for women of reproductive age (% of total women) Rate of malnutrition-related hospitalizations for children under 5 (gender disaggregated) (% of total children)
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Capacity Indicators assessments (available = 1, not available = 0) Compensation payments directly to women’s (bank) accounts (% of women who received compensation) Conflict management capacity (% of staff or people trained in conflict management) Equity and fairness (% of affected that received relief in the recent disaster) Extension services (no. of extension agents per 1000 people) Financial capacity (no. of banks catering to agriculture and poor communities) Insurance and other forms of security (presence = 1, absence = 0) Joint (husband and wife) land/housing titles granted (no. of such families) Literacy rate (% of population who can read and write) Longevity (no. of years) Per capita income (INR per capita per year) % of Adaptation Fund and bilateral adaptation resources that have been directed toward gender equality Physical assets that support diversification of livelihoods (no. of assets) Political stability (no. of out of turn elections in past 10 years) Presence of climate change adaptation and disaster risk reduction plans (presence= 1, absence = 0) Presence of climate change adaptation and disaster risk reduction policies (presence= 1, absence = 0)
Sensitivity Indicators Relative wage parity between men and women (% deviation from men wages) Sex-separated sanitation facilities (no. of sanitation facilities per 1000 households) Time spent by women in collection of water (hrs. per day) Violence against women during extreme events (% of women affected)
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Capacity Indicators Presence of general criteria for receiving climate change adaptation assistance (0= criteria is absent, 1 = present) Presence of meteorological services (including early warning, forecasts, crop advisory etc.) (presence = 1, absence = 0) Readiness to migrate (no. migrated in the previous year) Rural development programs (no. of rural development programs) Social groups (SHGs, water user associations, cooperatives, etc.) and networks (no. of such groups per 1,000 people) Staff trained on CCA and DRR issues (% of total staff) Women access to credit, insurance and other financial mechanisms (% of women with deposit or loan account) Women access to markets (km from nearby market) Women access to productive resources (% of women holding land tenure) Women employed in the total work force (% of women employed out of total workforce) Women house ownership (% of women with home ownership) Women in decision-making roles in panchayats, societies, etc. (% of bodies with women in leadership positions) Women’s access to agricultural extension services and technologies (% of women getting benefited from these services as a share of total women population) Women’s economic resilience (Per capita income or per capita savings)
Sensitivity Indicators Agriculture and Food % of cropped area in sensitive stages coinciding with the onset of hazards % of inferior infrastructure (e.g. flood protection, water storage, etc.)
Animal protein consumption per capita (% of total calories)
Change in planting time (no. of days from normal time) Coefficient of variation in cereal crop yields (%)
Dependency on market for fodder and food (% of fodder and food purchased from market)
Food insecurity (% of population with malnutrition and hunger) Households with rainfed agriculture dependence (% of total households) Population suffering from malnutrition (% of total population)
Projected decline in agricultural yield (% change)
Projected decline in traditional sources of food (% change)
Rainfall variability during cropping season (coefficient of variation from long term average, %) Share of agriculture and related sources of income (% of total income) Share of rainfed crops and cropping systems (% of arable area) Share of water intensive crops and cropping systems (% of total cropped area)
Capacity Indicators
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Agricultural capacity including indigenous knowledge (% of farmers trained) Amount of fresh water available for agriculture (% of total water) Area under improved food production practices (% of total cropped area) Crop diversification (no. of types of crops grown) Dependable irrigation systems (% of area under assured irrigation) Food availability (covers public distribution system, grain reserves and other social support systems) (kg per capita per year) Food production per capita (tons per capita) Irrigation incidence (% of area irrigated) Irrigation intensity (% of use of land equipped with irrigation) Projected increase in agriculture yields (% from 30-year average) Ratio of water available for agriculture (% of total water used for agriculture)
Sensitivity Indicators
Capacity Indicators
Water and sanitation % of inferior infrastructure (e.g. flood protection, water storage, etc.)
Development pressures (% of inland water used for household purposes) Development pressures (% of inland water used for industrial purposes) External freshwater dependency (% of total water consumed)
Incidence of waterborne diseases (% of population affected per annum) Internal freshwater dependency (% of total water consumed) Mortality due to waterborne diseases (% of total population)
People living in drought-prone areas (% of total population)
People living in flood-prone areas (% of total population) Proportion of poor quality water (% of total water bodies) Saltwater intrusion into groundwater (% of area affected by salinity) Stress on existing water resources (% of depleted water sources) Water consumption/demand (m3 ha-1) Water scarcity (% of population without access to drinking water)
Land and infrastructure Damage to communication infrastructure in the most recent cyclone (% damaged) 44
Amount of fresh water available for drinking (L per capita per year, 1,000) Diversified water sources (no. of sources) Drainage facilities (% of agriculture land equipped with drainage facilities) Groundwater availability (% of land with accessible groundwater aquifers) Management capacity (no. of water management techniques) Per capita water availability for drinking purposes (m3/yr.) Population with access to improved sanitation (% of total population) Population with access to improved water supply (% of total population)
Area under improved land management practices (% of total arable land)
Sensitivity Indicators Damage to schools in the most recent cyclone (% of schools damaged) Damage to transport infrastructure in the most recent cyclone (% of total transport infrastructure) Degraded/eroded land/soil compaction including coastal erosion (% of total land) Eroded coast (% of total coast line)
Land below sea level (% of total area) Land disturbed for economic activity (% of total area)
Land drought prone (% of total area)
Land flooded (% of total area) Land undulated/with gullies (% of total area) Landscape condition (% of land denuded) Surface water runoff (kg/m^2)
Fisheries and animal husbandry % of inferior infrastructure (e.g. flood protection, water storage, etc.)
Cattle vulnerable to outbreaks (% of total cattle population) Coefficient of variation in productivity (%) Decline in catch potential (% change from 30-year average) Dependency on fisheries (% of income from fisheries)
Diseases (Vector-borne, water-borne, etc.) (% income lost due to diseases per year)
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Capacity Indicators Availability of good quality land for dwelling (ha per capita) Coastal protection infrastructure (% length of coast protected by dykes) Good quality land available for cultivation (% of total land) Land available for evacuation (ha available per 1,000 people) Land cover/vegetation (% of land covered by vegetation) Population living in areas more than 10 m above sea-level (%) Share of land unmanaged (not used for economic activity) (% of total land) Soil organic matter content (%)
Access to extension services for better fish production (present=1, absent=0) Access to veterinary services (present=1, absent=0) Catch potential (tons per year) Fish reserves (area under conservation, %) Fodder availability (covers fodder trees, community reserves, etc.) (per capita fodder per annum, tons) Proportion of hardy fish/cattle/bird species (% of all species)
Sensitivity Indicators Fish vulnerable to outbreaks (% of total fish resources)
Projected decline in catch potential (% deviation from 30-year average) Proportion of poor quality water (as a % of total water used) Water consumption (inland fisheries) (m3 per ton of fish production, 1,000)
Biodiversity and ecosystems including forests Decline in aquatic biodiversity (% decline from 30-year average) Decline in bird and animal biodiversity (% decline from 30-year average) Decline in land vegetation biodiversity (% decline from 30-year average) Declining agro-biodiversity (% decline from 30-year average)
Dependency on forests for livelihoods (% of total population) Forest fires (% area affected by forest fires) Habitat fragmentation (% of area affected by fragmentation) Habitat loss (% area lost compared to 10 years ago) Invasive species (% of area affected by invasive species) Projected decline in biodiversity due to climate change (% decline from 30-year average) Projected change in land cover due to change in species (% decline from 30-year average) Share of intolerant species (% of all species) Shifting cultivation (% of total area) 46
Capacity Indicators Ratio of water available for animal husbandry (% of total water resources) Ratio of water available for fisheries (% of total water resources) Share of warm water species (%)
Area under conservation forestry (% of forest area) Area under organic farming (% of cultivated area)
Area under perennial/stable water bodies (% of total area)
Capacity for biodiversity conservation (% area under conservation) Distance from perineal/stable water bodies (km) Forest density (no. of trees per ha) Keystone species (no. of species per sq. km) Primary productivity (t/m2/yr.) Share of tolerant species (% of species) Species abundance (no. of individuals of a key species per ha) Species diversity (Shannon Index values)
Species richness (total no. of species) Wetlands (% of total land area)
5. GUIDELINES FOR USING VCAI 5.1 D ESCRIPTION OF THE VCAI T OOL Note: The reader is advised to refer to the accompanying excel tool while reading this section. The current version of the tool is filled with hypothetical data for hypothetical places to help the user understand how the tool works. The VCAI tool consists of several visible and hidden worksheets intuitively organized to help the user through the process of vulnerability assessment.
The tool is organized into several visible and hidden worksheets. The visible worksheets are Guide, Overview, Input_Exposure, Input_Sensitivity and Capacity, Input_Computations and Output. This organization was chosen so that the user can work through the tool with better understanding and convenience. The subsequent parts of this section will provide more details on individual worksheets, the inputs needed and the computations carried out in each one of them. There are two hidden worksheets within the tool: ESC Indicators and States&Districts. ESC Indicators provides the list of exposure, sensitivity and capacity indicators used in the tool organized into the following sectors: a) Social, institutional and policy dimensions; b) Agriculture and food; c) Water and sanitation; d) Land and infrastructure; e) Fisheries and animal husbandry; f) Biodiversity and ecosystems including forests. The States&Districts worksheet lists the administrative states and districts in India for filling the project information in the Overview worksheet. It also lists several details used in the Overview worksheet such as stage of the project, type of evaluation, disaggregated analysis, etc. Both these sheets are hidden and protected by a password and only NABARD staff has access to these sheets. Note: In the current version of the tool, the indicators sheet is hidden and it may be necessary to consult NABARD staff to make any changes to the indicators. 47
G UIDE
WORKSHEET
The Guide worksheet provides the general background and purpose of the VCAI tool as listed below: The VCAI tool provides vulnerability rankings of places within the project boundary to facilitate exante resource prioritization and ex-post monitoring and evaluation.
1. The Vulnerability and Capacity Assessment Index for Climate Change Adaptation of Natural Resource-based Communities (VCAI) is a tool for facilitating CCA in natural resource management which will assist project EEs to assess climate change vulnerability of areas within the proposed project boundary. Users will be able to assess vulnerabilities at sectorial level such as agriculture and water and at the overall level across sectors. 2. The VCAI tool provides vulnerability rankings of places within the project boundary to facilitate ex-ante resource prioritization and ex-post monitoring and evaluation. 3. The VCAI tool consists of a combination of normalized socioeconomic and bio-physical indicators to arrive at an index called VCAI. 4. The VCAI follows the popular approach of assessing climate change vulnerability using exposure, sensitivity and capacity elements. These elements were derived by component indicators obtained from the literature and consultations with experts and local communities (Please refer to Annexure 3). 5. Most indicators were selected considering measurability of indicators as criteria. For indicators where values are difficult to obtain, users are provided an option to replace them with a proxy indicator after consultation within the project team and the NABARD team. 6. The tool is designed in a flexible format so that users can add and remove indicators to see the sensitivity in VCAI. However, users are advised to do so with sound logic and only after thorough examination of pros and cons of replacing the indicators with new ones.
O VERVIEW
WORKSHEET
The VCAI tool was developed keeping in mind the various needs of EEs for implementing adaptation projects on the ground and the need for NABARD to monitor and evaluate project progress on the ground, including the possibility to compare performance of adaptation interventions across states and regions. In order to help with this objective, users are required to fill in the project details, geographical details, vulnerability assessment details 48
Using this tool, users can assess vulnerabilities at Household, Village/Comm unity, Block/ Panchayat Samiti, Panchayat, Zillah Parishad, District and State levels.
and go through a set of checklists before submitting the VCA results to NABARD in the Overview worksheet (Figure 8). Project Details:
Name and contact details of the Executing Entity: Provide contact details including phone and fax numbers, email ID and full postal address for communication. Project Title: Provide the title of the project even if tentative. Climate change context, objectives and problem statement: This is to help the monitoring agency to understand the climate change context of the project location, objectives of the project, the problem statement and adaptation outcomes. Classification of the project: Select the appropriate classification of the project using the drop-down menu. If the project does not fall in any of the provided categories, the user can still type the appropriate classification within the cell overriding the options provided below: o Climate change adaptation: Select this option if the project is mainly aimed at achieving climate change adaptation. o Climate proofing of development: Select this if the project aims to mainstream CCA into development. o Development: Select this option if the project is purely a development project with no consideration for climate change. o Disaster risk reduction: Select this if the project is mainly disaster risk reduction. o Climate change adaptation and disaster risk reduction: Select this if the project is a combination of both CCA and DRR initiatives. Start date of the project: The date of beginning of the project (or expected date). Duration of the project: The total duration of the project. Year of the project: Project year in which the current vulnerability assessment is being done.
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Figure 8: Screenshot of the Overview worksheet
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Geographic Details:
State: Select the state in which the project is located using the drop-down menu. The user has the option of typing multiple names separated by commas if the project is implemented in multiple states. District: Select the district in which the project is being implemented using the drop-down menu. The user has the option of typing multiple names separated by commas if the project is implemented in multiple districts.
Vulnerability Assessment Details:
Unit of vulnerability assessment: Select the appropriate unit at which the vulnerability assessment is carried out using the drop-down menu. Users can select among the following, with the option of typing multiple options: Block/Panchayat Samiti, District, Household, Panchayat, State, Village/Community, and Zillah Parishad. No. of sites selected for vulnerability assessment: This pertains to the total number of sites (vulnerability assessment units) selected for conducting the VCA. The maximum number of units the tool can accommodate is ten (10). It is important that users keep the number consistent in the rest of the worksheets as this number will populate the required number of columns for entering the data in the subsequent worksheets. Stage of assessment: Select the stage of the project to which the current assessment corresponds. Users have the following options: o Project baseline: Select this option if the current vulnerability assessment is to be used as a project baseline: First assessment; Second assessment; Third assessment; and Fourth assessment. o Post-project impact assessment: Select this option if the vulnerability assessment is conducted after the end of the project. In general, post-project impact assessments are conducted several years after completing the project depending on the nature of interventions. Total number of assessments to be done within the project: Select the total number of assessments to be conducted as a part of the project. It depends on the monitoring and 51
evaluation requirements agreed by the donor and implementing agencies (NABARD). If the EE is required to submit a progress report twice in a year and the project is implemented for three years, the total number of assessments to be done is six (6). For quarterly reports, the number will be 12 for a 3-year project. Number of the current assessment: Please select the number of the current assessment. This will be 1 if the assessment is being done for the first time. Disaggregated analysis: The Adaptation Fund emphasizes the vulnerability reduction for groups such as women, indigenous people and the poor. EEs are advised to conduct a group-specific VCA targeting these vulnerable groups since conducting a generic assessment based on a large sample of subjects may not clearly bring out the vulnerabilities of these specific groups which are often represented in smaller number and thus are masked when mixed with a large sample of other subjects. Though this could increase the cost of the VCA in terms of staff time, it will pay off in terms of targeting the interventions for reducing the vulnerability of these groups. Users are advised to select one of these options: Below-poverty line; Children; Displaced; Indigenous people; Landless; Migrants/settlements; Old people and Women. Vulnerabilities compared to the previous assessment: Provide a brief assessment of the current vulnerabilities compared to the previous assessments. Comment: Please provide a brief explanation for the above performance, reasons, what worked and what didn’t work.
Checklist: A checklist has been provided in the Overview worksheet to make sure important procedures were followed before submitting the Excel file to the NIE.
A checklist has been provided to make sure important procedures were followed before submitting the Excel file to the implementing agency (NABARD). Users are advised to check the following before submitting the Excel file for review: Identified and communicated with the focal point at NABARD on reporting requirements; Read the accompanying documentation to understand the vulnerability assessment methodology; Consulted literature to understand the hazard and climate change context of the project area; Conducted focus group discussion with the community members; Conducted focus group discussion with other important stakeholders; Decided the unit of analysis; Identified the vulnerable groups; Planned 52
the logistics for conducting household surveys for baseline data collection; Validated the data collected in FGDs using the secondary sources; and Completed the data entry without missing values. The final check box ‘Have used proxy or new indicators in place of the existing ones (Please fill the table below)’, which is marked red, is important especially if users have used proxy indicators (please refer to Annexure 4) or used indicators that are additional and new to the ones already included in the tool. This will be helpful for the implementing agency to review the additional indicators and incorporate them as and when a new version of the tool is developed. Upon checking this box, users are provided with a table to input new and proxy indicators against selected existing indicators.
I NPUT _E XPOSU RE The physical exposure related data is to be entered into the Input_Exposure worksheet.
W ORKSHEET
The Input_Exposure worksheet is the one where the user enters the data related to various physical exposures that the project sites are subjected to (please refer to Figure 9 for the screenshot of the Input_Exposure worksheet). As a first step, the user is required to enter the number of sites selected for conducting the vulnerability assessment. The tool can accommodate a maximum of 10 sites for which the user can enter the data against each indicator listed below. Entering this number is important as it will populate the number of columns required to feed the data with each column representing the unit of analysis selected in the Overview worksheet. Subsequently, the user is required to select the top 15 exposure indicators which are prioritized during the consultations with communities and other stakeholders engaged in the project. Within the tool, the Region consists of the average value of all the units of assessment. Note: After entering the number of sites for the vulnerability analysis, users are advised to click on the green ‘OK!’ button provided on the right side of the number box to populate the required number of columns for data entry below. Users are also required to click on the green ‘Perform Calculations!’ button provided at the bottom of the worksheet in order for Excel to perform the calculations within the worksheet. It is important to note that the row averages may not be updated without clicking this button. 53
Figure 9: Screenshot of the Exposure worksheet 54
Checklist: A checklist is provided at the bottom of the Input_Exposure worksheet for users to check if important points were observed while filling in the worksheet. It is important that users check all boxes before clicking on ‘Perform Calculations!’. The checklist highlights the following points: the No. of sites selected for vulnerability assessment is checked to be consistent throughout the tool; the OK button placed adjacent to the No. of sites was clicked; the Top 15 exposure indicators were prioritized using techniques in Annexure 7 of the accompanying report; The values for indicators obtained from the published sources were discussed with the communities; Missing values were left blank without leaving a space in the cells; and importantly Click on the Perform Calculations! button at the end of the sheet to perform calculations!
I NPUT _S ENSITIVITY W ORKSHEET Sensitivity and Capacity are assessed for six areas: social, institutional and policy dimensions, agriculture and food, water and sanitation, land and infrastructure, fisheries and animal husbandry and biodiversity and ecosystems including forests.
AND
C APACITY
In this worksheet, users are to input data related to the sensitivity and capacity indicators of VCAI (please refer to the screenshot in Figure 10). Sensitivity and Capacity elements are organized into six sectors: a) Social, institutional and policy dimensions; b) Agriculture and food; c) Water and sanitation; d) Land and infrastructure; e) Fisheries and animal husbandry; and f) Biodiversity and ecosystems including forests. The sensitivity and capacity in each of these sectors is assessed by users who select a set of prioritized indicators. Typically, users are required to identify and prioritize these indicators using focus group discussions with the community members (several participatory rural appraisal techniques are described in Annexure 7 of this report). The sequence of operations include, entering the number of sites selected for the vulnerability assessment, clicking on ‘OK!’, selecting and entering the data for all prioritized indicators and clicking on ‘ Perform Calculations!’ at the end of the worksheet. User input cells are marked with a double border and negative values are permissible. The row averages do not count blank cells and entering zero will be counted for calculating row averages. Users are advised to identify and prioritize at least five indicators in each of the vulnerability element while the maximum number of indicators is set at 10.
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Figure 10: Screenshot of Input_Sensitivity and Capacity worksheet 56
Checklist: A checklist similar to the previous worksheet is provided at the bottom of the Input_Sensitivity and Capacity worksheet for users to check if important points were observed while filling in the worksheet. It is important that users check all boxes before clicking on ‘Perform Calculations!’ The checklist highlights: The No. of sites selected for vulnerability assessment is checked to be consistent throughout the tool; The OK! button placed adjacent to the No. of sites was clicked; Top 15 exposure indicators were prioritized using techniques in Annexure 7 of the accompanying report; The values for indicators obtained from the published sources were discussed with the communities; Missing values were left blank without leaving a space in the cells; and importantly Click on the Perform Calculations! button below for the sheet to perform calculations!
I NPUT _C OMPUTATIONS
WORKSHEET
The Input_Computations worksheet normalizes the indicators following the linear normalization procedure using the minimum and maximum thresholds. The values for minimum and maximum thresholds can either be obtained from secondary sources (i.e. published literature), from expert elicitation or from the goals agreed among the project stakeholders. Ideally, threshold values are fixed at the beginning of the project and hence are not expected to be changed over the course of the project. User input cells are marked with a double border and negative values are permissible (please refer to Figure 11 for the layout of the worksheet). Note: For indicators whose threshold values are not available, users are advised to estimate these thresholds at the extreme possible limits to avoid the actual measured values falling beyond these limits. For example, users can choose to input 0 for minimum and 100 for maximum threshold for all %related indicators.
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Figure 11: Screenshot of Input_Computations worksheet
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Checklist: Users are requested to check all boxes on the checklist provided at the bottom of the Input_Computations worksheet to make sure that important points were observed while entering the data in the worksheet. The checklist highlights: The No. of sites selected for vulnerability assessment is checked to be consistent throughout the tool; The OK button placed adjacent to the No. of sites was clicked; The obtained threshold values from published and community sources are checked for possible errors; No cells are left blank; and importantly Click on the Perform Calculations! button below for the sheet to perform calculations!
O UTPUT W ORKSHEET The Output worksheet presents exposure, sensitivity and capacity values and the vulnerability values in the form of data and in graphical format. These values will assist in identifying the most vulnerable areas within the project boundary and can also be used for crossproject comparisons at the implementation agency level. No values in this worksheet can be changed by users! Exposure Exposure is assessed independently for all the sites and presented comparatively using a bar chart (Figure 12). The scale of the Y axis will automatically adjust depending on the actual values with the maximum set at 1. The exposure chart helps to compare places across the region and pinpoint the place with the highest and least exposure. Sensitivity, Capacity and Vulnerability Sensitivity, capacity and vulnerability within individual sectors are depicted using Radar diagrams (Figure 13). Using these diagrams, users will be able to decipher how sensitivity and capacity determines the overall vulnerability. In addition, output is also provided for comparing areas in exposure, sensitivity, capacity and vulnerabilities for all the locations (Figure 14) and numerical output of all the computations made in the tool (Figure 15).
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Figure 12: Graphical output of exposer in the Output worksheet 60
Figure 13: Graphical output of sensitivity, capacity and vulnerabilities in different sectors
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Figure 14: Graphs comparing overall rankings of places 62
Figure 15: Data output provided by the VCAI tool
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5.2 T HE PLACE OF VCAI TOOL IN A PROJECT The indicators are fixed in the methodology and are not expected to be changed by the executing entities.
The ultimate purpose of carrying out VCAs is to understand the factors underlying the vulnerabilities to climate change in terms of exposure, sensitivity and capacities leading to better design of the adaptation interventions. The place of climate change VCAs in the overall adaptation project planning is shown in Figure 16. It is clear from the figure that the common steps involved leading to implementation of a properly designed adaptation project consist of seven generic steps which include defining the objective of adaptation interventions, identifying climate trends, assessing vulnerabilities, assessing risks, identifying and assessing adaptation interventions, implementing adaptation interventions, and monitoring and evaluation. Though the scope of this document does not cover the entire cycle of adaptation planning, it is highly contextual and useful to provide some way forward on prioritizing adaptation options that are readily available to be implemented by various stakeholders (See Annexure 8).
Figure 16: Steps involved in developing adaptation strategy 64
5.3 I NDICATOR PRIORITIZATION The indicators employed in the VCAI were identified following a thorough process of reviewing the literature, community consultations.
The indicators employed in the VCAI were identified following a thorough process of literature review, community consultations, and consultations with EEs and NABARD staff. The indicators are fixed in the methodology and are not expected to be changed by the individual EEs under normal circumstances. This will provide NABARD with the ability to compare projects across different agro-climatic areas in terms of the effectiveness of adaptation projects. However, considerable flexibility has been provided to the EEs to prioritize a few indicators from a basket of indicators provided in the tool. In the current version, users can prioritize up to a maximum of 15 indicators in the exposure element and 10 indicators in the sensitivity and capacity elements. Within these 10 indicators, though not advocated, users can introduce new indicators by directly typing into the cells. In such a situation, it is essential that users provide details of proxy and new indicators in the Overview sheet.
5.4 W EIGHTS FOR INDICATORS AND SECTORS All indicators and sectors are equally weighted in the VCAI tool.
All indicators and sectors are equally weighted in the VCAI tool in order to avoid subjectivity in giving weights, leading to misleading conclusions and losing the comparability of VCAI values across projects and locations. This was done by keeping to the following principles:
All sectors play some role in the overall wellbeing, and giving less weight to a sector could be misconstrued as giving it less importance, which is not the case. A sector that is the least performing, even if ranked least important, can still be a limiting factor following the ‘law of limiting factors’.9 While conducting the vulnerability assessment of a household, a village or a group of villages, the above factors play a stronger role than for an isolated household or village.
9
The law of limiting factors in this context states that the performance of a system is limited by the performance of the least performing part of the system.
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5.5 U NIT OF V ULNERABILITY A SSESSMENT The unit of the vulnerability assessment refers to the scale at which a VCA is conducted and also the scale at which the data is collected and inputted into the VCAI tool.
The unit of the vulnerability assessment refers to the scale at which the EEs would like to conduct the VCAs and also the scale at which the data is collected and inputted into the VCAI tool. For example, the unit of analysis could be household, village, block, Zillah Parishad, District and State. Within the VCAI tool, users can select the appropriate unit of analysis in the Overview worksheet. Though the primary focus of the VCA is at the project level, it is important to recognize the fact that project performance can often be impacted by the policy and institutional environment within which it operates. Annexure 5 provides a list of indicators pertinent to policy and institutional levels. The VCAI tool incorporates these indicators into the analysis and EEs are encouraged to consider these indicators in the VCAs if deemed necessary. Note: While reporting the VCAI results to NABARD, EEs are encouraged to adequately define and explain the boundary conditions within which the project is implemented, as it is essential to understand the differences in project performances, especially if it operates across multiple states with different policy and institutional environments.
5.6 S AMPLE SIZE After deciding on the unit of the vulnerability assessment, the sample size constitutes an important aspect of conducting the vulnerability assessment. It is recommended to use statistical sampling techniques to identify the sample size of households. Please refer to Annexure 6.
5.7 C OLLECTING DATA FOR INDICATORS Collecting data for indicators is an important aspect of the VCAI and hence every precaution needs to be taken to ensure the quality of the data used. Values for most indicators may be obtained from local data sources such as district and sub-district administrative offices. However, values for several other indicators may need to be obtained directly from the communities for which participatory rural appraisal methods are 66
advised to be followed. Conducting vulnerability assessments in a participatory manner helps in increasing the acceptability and ownership of the project while ensuring that the project is relevant to the location. Table 6 lists the suggested PRA methods, which are briefly described in Annexure 7. Table 6: List of PRA techniques that can be used for conducting VCAI
List of PRA techniques Communication maps Problem/preference ranking Cross impacts analysis Rain calendars Focus group discussions Ranking Gender audit Resource maps Gender analysis Seasonal calendar Hazard impact on livelihood Social maps matrix Transect walks Hazard mapping Venn diagrams Hazard trend analysis Vulnerability and capacity Mental models matrix Participatory scenario Wealth ranking development Power mapping
5.8 G ENDER CONSIDERATIONS The VCAI includes several gender-specific vulnerability indicators and several other indicators which are sensitive to the same factors that impact gender disparities in the society.
Groups such as women and children have been identified as some of the most vulnerable to climate change for which the vulnerabilities are to be assessed before the project is implemented. Hence, it is important that the EEs consider conducting sex-disaggregated VCAs using stratified random sampling or conducting focus group discussions with vulnerable sections of the society. Vulnerability assessments are important for the specific groups that are disproportionately impacted by climate change. It is highly recommended, if resources permit, to conduct a disaggregated VCA especially for the most vulnerable groups. Specific efforts were made to make sure that the VCAI addresses gender issues through the inclusion of several gender-specific vulnerability indicators (Table 7). Several other indicators included in the index are sensitive to the same factors that impact gender disparities in society (e.g. indicators that are related to access to productive assets and resources).
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Table 7: Gender-specific vulnerability indicators
Sensitivity % of land area with inferior infrastructure (e.g. flood protection) within informal settlements Proportion of women-headed households Time spent in collection of water Distance travelled for collecting water Relative wage parity between men and women Rate of malnutrition-related hospitalizations for children under 5 (gender disaggregated) Rate of anemia prevalence for women of reproductive age Violence against women during extreme events Sex-separated sanitation facilities Access to energy by gender
Capacity Proportion of women in decision making roles (e.g. panchayats, societies, associations, producer groups, board members, etc.) Women access to productive resources (e.g. land tenure held by women) Women access to credit, insurance and other financial mechanisms Women access to markets Women house ownership No. of joint (husband and wife) land/housing titles granted Amount of compensation payments directly to women’s (bank) accounts Women’s economic resilience (e.g. income and savings) % of women employed in the total work force Proportion of women involved in climate change adaptation planning, for example, DRR, state adaptation plans, local disaster prevention planning, etc. Proportion of women in high -level decision-making roles related to climate change adaptation, for example, in the ministry of environment, forestry and climate change Women’s access to agricultural extension services and technologies.
Gender issues are better evaluated and incorporated into vulnerability assessments by conducting community consultations through participatory approaches discussed in Annexure 7. Participatory tools such as power mapping, Venn diagrams, focus group discussions and scenario analysis can be of particular use here. The purpose of consultations should be to understand prevailing gender sensitivities, to evaluate if project interventions will exacerbate the sensitivities, and to discuss if the included set of indicators suffices to capture the genderrelated issues in the vulnerability assessments. 68
5.9 F ILLING IN INDICATOR VALUES It is important that EEs take necessary precautions while filling in the indicator values for the purpose of their own monitoring and evaluation needs and for reporting to NABARD. The following precautions are advised: 1. Fill in the indicator values in the units required by the index. If the data is available in different units, a conversion is necessary. 2. Make every effort to include only those indicators for which the data could be collected. In cases where the data are not available for specific indicators, EEs are requested to use values of proxy indicators or obtain values through expert elicitation (an indicative list of proxy indicators is provided in Annexure 4). If none of this is possible, EEs could leave the cells blank.
5.10 I NTERPRETING THE VCAI OUTPUTS 1. The VCAI tool provides users with the ability to measure Negative VCAI vulnerability at different levels: households; individual values indicate that the village level and the level of a collection of villages (e.g. block capacities are or Zillah Parishad or district). higher than the 2. The tool also provides the ability to measure the vulnerability sum of exposures at specific sector levels and at aggregate levels. This ability and sensitivities. was provided keeping in view the broad range of adaptation Zero indicates projects that can be implemented by NABARD and its EEs, lowest which vary in their geographical scale and sectoral focus. vulnerability and 3. The VCAI output at each level and aggregation of the above 1 indicates levels is normalized to always provide VCAI output ranging highest anywhere between -1 and +1. Negative values indicate that vulnerability. the sum of capacities is higher than the sum of exposures and sensitivities. Zero indicates the lowest vulnerability and 1 indicates the highest vulnerability. By reading these values and the values of the underlying indicators, one is able to discern the reasons why a particular place or sector is performing poorly compared to other locations. This way, the user will be able to design the project by introducing appropriate interventions to address the specific vulnerabilities. The negative scale was adopted after consultations with NABARD staff. 69
5.11 U SING VCAI FOR M&E 1. EEs will be able to use the VCAI tool for both ex-ante and ex-post evaluation purposes. For this, users are required to identify the baseline period, which could be the year before the actual start of the project interventions. Comparison of VCAI values during the course of the project will indicate project performance in reducing the vulnerabilities. 2. The VCAI tool can also be used to assess project effectiveness. Figure 17 provides a graphic of how users can assess project effectiveness using the VCAI.
Figure 17: Employing VCAI for project M&E purposes
Pex Pc1 Pc0 Where: Pex: Effectiveness of project x; Pc0, Pc1, Pc3: VCAI values at times T1, T2 and T3, respectively Ix, Iy, Iz: Project interventions at time T1, T2 and T3, respectively (if applicable as in the case of multi-stage projects).
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Note: It is important to note that the VCAI tool does not directly isolate the effects of larger developmental policy and institutional processes occurring outside the purview of the adaptation project being implemented by EEs. It is possible that part of the changes in vulnerabilities assessed at later stages of project implementation could have been influenced by extraneous processes that happen outside the purview of the project. Hence, EEs are recommended to be cognizant of extraneous factors influencing projectlevel vulnerability and consider them while interpreting the VCA and describing the project performance at various stages of reporting.
5.12 W HAT IS NEXT AFTER VULNERABILITY ASSESSMENT ? Better understanding of factors underlying the vulnerabilities to climate change leads to better design of adaptation interventions.
A vulnerability assessment is not an end in itself but is a means to understand the causal factors which can help identify what works best to address the identified vulnerabilities. Hence, vulnerability assessment exercises should naturally lead to identifying adaptation interventions that will effectively address the vulnerabilities identified and quantified. This can be done by combining the VCA exercise with a multi-criteria methodology process that will use the sensitivity indicators prioritized as a part of the VCA to prioritize adaptation interventions. While identifying adaptation interventions is beyond the scope of this report and the VCAI tool, succinct information is provided on the application of the Analytic Hierarchy Process (AHP) in Annexure 8. From the annexure, it can be noted that the AHP provides a robust means of prioritizing adaptation options. It allows users to combine the multiple criteria and indicators that the stakeholders have used in assessing the adaptation options. The AHP is compatible with the PRA techniques that are employed to conduct vulnerability assessments. For the reason of simplicity, it is suggested that the process of VCA and prioritization of adaptation interventions using AHP techniques could be combined into a single exercise. The schematic for such an integrated approach is shown in Figure 18. It is important to understand that the prioritization of
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adaptation actions can be done using several approaches and AHP is just one of them. Depending on the methodology selected by the project and the resources available (number of support staff, funds, etc.), the project could either decide to combine the VCA and AHP together in the same PRA exercise or conduct them separately. It is also important to take note that conducting a AHP exercise in a participatory set-up takes anywhere between 2-3 hours for a set of 3-4 criteria, 5-8 indicators and 4-5 interventions to be evaluated in a group of 15-20 participants. The time taken can increase depending on the education levels of the participants and how well versed the moderator is with the methodology and moderation skills.
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Figure 18: Unified process for conducting vulnerability assessment and prioritization of adaptation options 73
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6. MOCK EXERCISE This chapter provides a simple example on how to conduct the vulnerability and capacity assessment using the VCAI tool for a hypothetical context and data.
6.1 C ONTEXT A hypothetical project on CCA for addressing flood risks is planned to be implemented in two Blocks with names Place 1 and Place 2 (please refer to the Excel tool for the data on the respective locations). These two Blocks differ in their socioeconomic backgrounds and hazard contexts. Place 1 has moderately high exposure due to its physical location in a lowlying area near to a river, while Place 2 is situated far from the river, at a relatively higher elevation. Although the Executing Entity (EE) is aware of the broad vulnerability contexts of these two Blocks, it wants to gain better understanding on how sensitivities and capacities compare in the Blocks so that appropriate priority can be given to addressing their needs. Hence, comparing the vulnerabilities of these two locations is of paramount importance for the EE.
6.2 P ARTICIPATORY E XERCISE The data for vulnerability assessment were collected by organizing focus group discussions (FGDs) with participants drawn from both male and female sections representing all possible socio-economic backgrounds in the two Blocks. Stratified random sampling was used to identify the participants. Separate FGDs were organized for both Blocks. The purpose of the FGD is to familiarize the stakeholders on various indicators being used to assess vulnerability in the VCAI tool, assess the need for including additional indicators, obtain the data that could be available from the community, and familiarize the stakeholders about important vulnerabilities and solutions to address the vulnerabilities. FGDs were organized with the help of a moderator who is well versed with participatory rural appraisal (PRA) techniques, who has a good understanding of local conditions, and who masters
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the local language. FGDs were carried out using the following four steps. Step 1: The moderator introduces the purpose of the exercise and the objectives of the proposed project and leads the discussion on broader developmental and climate issues that need attention. Further, the moderator explains the concepts involved in vulnerability assessments such as exposure, sensitivity and capacity, how these influence the overall wellbeing of the Block, and how the vulnerability information can be used for better design and implementation of an adaptation project. Step 2: The participants were asked several questions related to the hazard context of the Block; for example, frequency and intensity of floods and long-term trends in their impacts. After the overall discussion on the hazard context, the participants were asked to rank the important impacts of recent past floods. The participants were then taken through a thorough discussion on a set of exposure indicators that were presented in the VCAI tool using charts. These indicators were discussed and agreed upon and the top 15 indicators were prioritized in order of importance (please see the preceding Figure 9). The data for simple indicators, such as duration of droughts, duration of floods, etc., were obtained from community members, while the data for the rest of the exposure indicators were obtained from the local meteorological department or from published records. Note: Please note that the VCAI treats all indicators equally. However, from the experience of the author, it is widely accepted that the prioritizing of indicators helps instill a more robust and deeper understanding of the indicators by the participants. Such an understanding is important since these indicators will form the basis for decision-making throughout the course of project implementation. Step 3: Subsequently, the facilitator asks a series of questions on what conditions may have predisposed the Block to the impacts discussed in the previous step by showing the list of sensitivity indicators included in the VCAI tool. During the discussion, FGD participants discuss and agree on the indicators included in the VCAI tool and a narrow set of 10 indicators were identified and ranked. The prioritized sensitivity indicators for social, institutional and policy dimensions are presented in the 76
preceding Figure 10. In the subsequent discussions, the participants rank the exposure indicators for other sectors including agriculture, land and infrastructure, water and sanitation, etc. The data for indicators, other than those that can be obtained from published records and formal sources, such as distance travelled by women for collecting water, health expenditures derived from loans, etc., were obtained from the FGD. Step 4: This step consists of the ways and means through which communities coped with the impacts of climate change discussed in Step 1. The communities’ existing capacities and those that they could mobilize within a short period of time were identified from the list of capacity indicators listed in the VCAI tool. The top 10 capacity indicators were prioritized and data that can be obtained from communities were collected for further entry into the VCAI tool.
6.3 C ALCULATIONS An effort is made here to carry out the calculations of a simple example of how to apply the VCAI tool.
C ALCULATION
OF
E XPOSURE
Using Equation 2, the exposure data for each Block, Place 1 and Place 2, is first transformed into a unit-less format.
Normalized indicator value
zi
xi Tmin ( x) Tmax ( x) Tmin ( x)
…………………………………………………………Equation 2 Exposure Indicator Climate variability, max. temperature (coefficient of variation in the past 30 years, %)
Place 1 Place 2 5
5
The data for the above exposure indicator can be obtained from published records such as National Communications, National Adaptation Plan of Action, or peer reviewed journal articles. Since the indicator ‘Climate variability, max temperature (coefficient of variation in the past 30 years, %)’ is measured as a percent, the minimum and maximum threshold values for this 77
indicator could be chosen as 0 and 100, respectively (refer to the preceding Figure 11). The transformed values can be obtained by replacing the respective values in Equation 2 as shown below. 5−0
Transformed value = 100−0 = 0.05 Note that the transformed value is the same for both Blocks due to identical indicator values. The Exposure (E) for each Block is given as average of all transformed exposure indicators. Table 8: Exposure of Blocks Place 1 and Place 2 Indicator Place 1 Climate variability, max temperature (coefficient of variation in the 0.05 past 30 years, %) Climate variability, min temperature (coefficient variation in the past 0.08 30 years, %) Climate variability, rainfall (coefficient of variation in the past 30 0.00 years, %) Air quality (air quality index, peak value in a year) 0.30 Duration of cyclones (average length per event in the past 30 years, 0.10 days) Delay in onset of monsoons (no. of days from the normal onset day, 0.10 average of the past 30 years) Duration of droughts (average length per event in the past 30 years) 0.34 Duration of floods (average length per event in the past 30 years) 0.03 Frost during critical stages of crop (no. of frost days) 0.05 Intensity of cyclones (peak wind speed in km/h) 0.25 Intensity of droughts (% reduction in RF from a 30-year average) 0.20 Intensity of floods (height of flood in m) 0.10 Land with elevation less than 10 m above sea-level (% of total land 0.20 area) Number of cold days above threshold (% of total days in a year) 0.10 Number of warm days above threshold (% of total days in a year) 0.40 Average (Exposure, E) = 0.15
Place 2 0.05 0.08 0.00 0.10 0.10 0.10 0.34 0.00 0.05 0.25 0.20 0.20 0.20 0.10 0.40 0.14
Average (Exposure, E) is calculated by summing all the indicator values for a given place and dividing by the number of indicator values.
C ALCULATION
OF
S ENSITIVITY
Similar to Exposure, the computation of Sensitivity (S) is done by first transforming the Sensitivity indicator values. The average of all transformed values will be the Sensitivity value for a given area (the VCAI consists of 6 areas; please refer to the ‘Methodology for Computing the VCAI’ sub-section). 78
Sensitivity Indicator (Social, institutional and policy) % of informal settlements in disaster-prone areas
Place 1 Place 2 20
20
Since the indicator is measured as a percent, the minimum and maximum threshold values for this indicator can be chosen as 0 and 100, respectively. Hence, the transformed sensitivity values are given as: 20−0
Transformed value = 100−0 = 0.20 Note that the transformed value is the same for both Blocks due to identical indicator values. A similar approach is to be followed for obtaining the Sensitivity values for agriculture and food, water and sanitation, land and infrastructure, fisheries and animal husbandry and biodiversity and ecosystems. Overall, Sensitivity (S) for each Block is obtained by averaging the transformed Sensitivity values as given below. Table 9: Sensitivity of Blocks Place 1 and Place 2 Sensitivity indicators (Social, Institutional and Policy) % of informal settlements in disaster-prone areas % of population living in kaccha and semi-pucca houses (huts or thatched houses) Diseases (vector-borne, water-borne, etc.) (affected, % of total population per annum) Disruption of public services during disasters (communities that received services, % of normal time) Distance traveled by women for collecting water (km per day) Estimated impact of future climate change on deaths from disease (projected deaths, % of population) Failure of communication facilities during disasters (% of affected receiving early warning or other forms of communication) Female recipients of post-disaster recovery funds and material assistance (% of total recipients) Health expenditure derived from loans (% of total health expenses) Illiteracy rate (% of total population) Average (Sensitivity, S)=
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Place 1 0.20
Place 2 0.20
0.50
0.50
0.15
0.15
0.80
0.80
0.51 0.02
0.51 0.02
0.20
0.50
0.45
0.60
0.35 0.45 0.36
0.05 0.15 0.35
C ALCULATION
OF
C APACITY
Capacity Indicator (Social, institutional and policy) % of population living in pucca houses (houses using concrete and steel)
Place 1 Place 2 25
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The value of above capacity indicator can be obtained through local government records or from the FGDs. Being a percentage, the minimum and maximum threshold values for this indicator are considered as 0 and 100, respectively. Hence, the transformed sensitivity values are given as 25−0
Transformed value for Place 1 = 100−0 =0.25 65−0
Transformed value for Place 2 = 100−0 =0.65 Overall, Capacity (C) for each Block is obtained by averaging the transformed Capacity values as given below. Table 10: Capacities of Blocks Place 1 and Place 2 Capacity indicators (Social, institutional and policy) Place 1 % of population living in pucca houses (houses using concrete and 0.25 steel) % of women in high-level decision-making roles related to CCA (e.g. 0.02 in the Ministry of Environment, Forestry, and Climate Change, etc.) % of women involved in CCA planning (e.g. DRR, state adaptation 0.15 plans, local disaster prevention planning, etc.) Access to CCA technologies (presence of CCA technologies= 1, 0.00 absence = 0) Access to credit (people with deposit or loan accounts, % of total 0.25 population) Access to energy by gender (% of female-headed households with 0.65 access to energy) Access to health (no. of hospitals per 1,000 people) 0.04 Access to markets (marketing costs, % of total revenue) 0.15 Access to natural resources (land, forests, water, etc.) (% of people 0.35 with land tenure) Alternative employment/livelihood opportunities that are not 0.03 directly impacted by climate (no. of alternative livelihoods available) Average (Capacity, C)= 0.19
Place 2 0.65 0.04 0.35 0.00 0.40 0.80 0.06 0.05 0.55 0.05 0.30
A similar approach is followed for obtaining the Capacity values for agriculture and food, water and sanitation, land and infrastructure, fisheries and animal husbandry and biodiversity and ecosystems. 80
Table 11: Sensitivity and Capacity of Blocks Place 1 and Place 2 Area Social, institutional and policy Agriculture and food Water and sanitation Land and infrastructure Fishery and animal husbandry Biodiversity and ecosystems Overall
Component Sensitivity Capacity Sensitivity
Place 1 0.36 0.19 0.30
Place 2 0.35 0.30 0.14
Capacity Sensitivity Capacity Sensitivity Capacity Sensitivity Capacity
0.38 0.32 0.18 0.35 0.13 0.25 0.43
0.52 0.25 0.34 0.18 0.17 0.08 0.51
Sensitivity Capacity Sensitivity Capacity
0.28 0.17 0.31 0.24
0.08 0.28 0.18 0.35
C ALCULATION OF V ULNERABILITY C APACITY I NDEX (VCAI)
AND
From Equation 1, the VCAI can be calculated as given below: Vulnerability index (VCAI) = (E+S)-C …………………………………………………………Equation 1 VCAI for Social, institutional and policy component of Place 1 = (0.15+0.36)-0.19=0.32. Similarly, the VCAI for all the other components is calculated and presented in the table below. The overall VCAI constitutes the average of the VCAI of the individual components. Table 12: VCAI values by area for Blocks Place 1 and Place 2 Area Social, institutional and policy Agriculture and food Water and sanitation Land and infrastructure Fishery and animal husbandry Biodiversity and ecosystems Overall VCAI=
Place 1 0.32 0.08 0.29 0.38 -0.03 0.26 0.22
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Place 2 0.20 -0.24 0.05 0.15 -0.29 -0.05 -0.03
6.4 I NTERPRETATION OF RESULTS Comparing both Blocks, Place 1 has much higher VCAI values, which signify that the Place 1 is more vulnerable than Place 2 and hence requires additional attention in terms of adaptation efforts. Place 1 has nearly 7 times the vulnerability of Place 2 due to overall lower capacity and higher sensitivity. The negative VCAI value for Place 2 should not be interpreted as not requiring adaptation interventions, since Place 2 still has considerable overall sensitivity (0.18), though this is smaller than Place 1 (0.31). The adaptation interventions that need to be implemented in Place 1 could differ from Place 2, targeting the specific sensitivities and complementing the specific capacities shown in the respective Blocks.
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7. C ONCLUSIONS It is evident from this exercise that there are several climate change VCA methodologies either published or used as tools by different developmental agencies. These methodologies vary widely in the approaches and tools employed in assessing vulnerabilities. However, there are a few commonalities that could be clearly identified which include adopting the model of exposure, sensitivity and capacity and following participatory approaches of some nature to identify and quantify vulnerability indicators. Developing a VCA methodology for an agency such as National Bank for Agriculture and Rural Development (NABARD) is fraught with challenges, most of which emanate from the fact that EEs which develop and implement adaptation projects are at different levels of capacity to understand and implement a robust VCA methodology. Hence, it was essential that the methodology developed for NABARD satisfy the requirements of simplicity while still being robust enough to be able to assess vulnerabilities with a high level of precision. Additional requirements, such as being able to compare results across locations, availability of data for indicators identified, and relevance to the local conditions, made the process of developing the methodology even more challenging. As described in the methodology sub-section of this report, it was the intent of the current methodology that it is able to meet most of these requirements. The methodology was developed in a consultative manner, based on field exercises and a feedback process. It was made simple enough so that most agencies engaged in development can understand the underlying math. This tool will continue to evolve at regular intervals, as and when new understanding and better tools are made available for decision-makers. We would greatly appreciate NABARD’s feedback on various aspects of this tool, which will contribute to its continuous evolution benefiting a range of EEs and ultimately the communities, who are the beneficiaries of the resultant adaptation projects.
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Klein, R.J.T. (2002) Climate change, adaptive capacity and sustainable development. Paper presented at the OECD Expert Meeting on Development and Climate Change, Paris, France. 13-14 March 2002. Paris, France: OECD. Luers, A.L., D.B. Lobell, L.S. Sklar, C.L. Addams and P.A. Matson (2003) A method for quantifying vulnerability, applied to the agricultural system of the Yaqui Valley, Mexico. Global Environmental Change, 13: 255-267. McCarthy, J.J., O.F. Canziani, N.A. Leary, D.J.Dokken and K.S. White (2001) Climate Change 2001: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press. Ministry of Environment and Forest (2010) Climate Change and India: A 4x4 Assessment. A Sectoral and Regional Analysis for 2030s. New Delhi, India: Ministry of Environment and Forests, Government of India. Pelling, M. (2008) International indicators for disaster risk: Lessons learnt and ways forward for adaptation metrics. Paper presented at Expert Consultation on Adaptation Metrics, 17-18 April 2008, Toshi Center Hotel, Tokyo, Japan. Hayama, Japan: Institute for Global Environmental Strategies. Prabhakar, S.V.R.K. (2011) Financial innovations and market mechanisms at the national level for coping with climate change. Paper presented at International Conference on Adaptation to Climate Change and Food Security in West Asia and North Africa, 13-16 Nov 2011, Kuwait City, Kuwait: WMO, KMD, AARINEA, KISR, FAO, ICARDA and OSU. Prabhakar, S.V.R.K. (2013) Climate change adaptation and developing countries. Lecture delivered at Keio University Seminar Course on 15-11-2013. Yokohama, Japan: Keio University. Prabhakar, S.V.R.K. and A. Srinivasan (2010) Metrics for mainstreaming adaptation in agriculture sector. In: Rattan Lal, Mannava V.K. Sivakumar, S.M.A. Faiz, A.H.M. Mustafizur Rahman and Khandakar R. Islam (eds.), Climate Change and Food Security in South Asia, USA: Ohio State University, World Meteorological Organization and Springer Ltd. Prabhakar, S.V.R.K., J. J. Pereira, J.M. Pulhin, G.S. Rao, H. Scheyvens and J. Cummins (2015) Effectiveness of insurance for disaster risk reduction and climate change 86
adaptation: Challenges and opportunities. IGES Research Report No 2015-01. Hayama, Japan: Institute for Global Environmental Strategies. Practical Action, IUCN, CECI and NAVIN (2010) Review of community based vulnerability assessment methods and tools. Kathmandu, Nepal: Ministry of Environment, Nepal. Available at http://www.climatenepal.org.np/main/?p=research&sp= onlinelibrary&opt=detail&id=282. Prowse, M. (2003) Towards a clearer understanding of ‘vulnerability’ in relation to chronic poverty. CPRC Working Paper 24. Manchester, UK: Chronic Poverty Research Centre. Rosenzweig, C. and F.N. Tubiello (2006) Developing climate change impacts and adaptation metrics for agriculture. Paper presented at Global Forum on Sustainable Development on the Economic Benefits of Climate Change Policies, 6-7 July 2006, Paris, France. Schneider, S.H., S. Semenov, A. Patwardhan, I. Burton, C.H.D. Magadza, M. Oppenheimer, A.B. Pittock, A. Rahman, J.B. Smith, A. Suarez and F. Yamin (2007) Assessing key vulnerabilities and the risk from climate change. In: M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson (eds.), Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge, UK: Cambridge University Press. Setiyadi, N. J. Birkmann and P. Buckle (2010) Disaster risk reduction and climate change adaptation: Case studies from South and South East Asia. Bonn, Germany: UNUEHS. Sharma, B. (2012) Assessing vulnerability to climate change using GIS. Geospatial World Weekly, 2012. Available at http://www.geospatialworld.net/Paper/Application/Artic leView.aspx?aid=30395. Smit, B., I. Burton, R.J.T. Klein and J. Wandel (2000) An anatomy of adaptation to climate change and variability. Climate Change, 45 (1): 223-251. SPREP (2009) Pacific climate change roundtable 2009. Available at http://www.sprep.org/table/pacific-climatechange-roundtable/. Srinivarsan, A. and S.V.R.K. Prabhakar (2009) Measures of adaptation to climatic change and variability (Adaptation Metrics). Report for World Bank. Tokyo, Japan: IGES. 87
Suarez, J. and P. Ribot (2003) Climate information as a neoclassical approach to risk? The case for addressing the root causes of vulnerability. Paper presented at the Third Annual Disaster Prevention Research Institute (DPRI) of the Kyoto University and the International Institute for Applied Systems Analysis (IIASA) on Integrated Disaster Risk Management: Coping with Regional Vulnerability, 3–5 July, Kyoto, Japan. Tambe, S., M.L. Arravatia, N.T. Bhutia and B. Swaroop (2011) Rapid, cost-effective and high resolution assessment of climate-related vulnerability of rural communities of Sikkim Himalaya, India. Current Science, 101 (2): 165-173. UNISDR (2015) Terminology on DRR. Available at http://www.unisdr.org/we/inform/terminology. United Nations (2012) Climate change and disaster risk reduction. TST Issue Brief. Geneva, Switzerland: United Nations. Vincent, K. and T. Cull (2014) Using indicators to assess climate change vulnerabilities: Are there lessons to learn from emerging loss and damage debates? Geography Compass, 8 (1): 1-12. World Food Program (2011) Building resilience: Bridging food security, climate change adaptation and disaster risk reduction. An overview of workshop case studies. Rome: World Food Program and Swiss Agency for Development and Cooperation. WWF (2011) Vulnerability assessment of people, livelihoods and ecosystems in the Ganga Basin. New Delhi, India: WWF. Available at http://www.wwfindia.org/wwf_publications/?6323/Vuln erability-Assessment-of-People. Yohe, G. and S.J. Tol (2002) Indicators for social and economic coping capacity—moving toward a working definition of adaptive capacity. Global Environmental Change, 12: 2540.
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9. ANNEXURES
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ANNEXURE 1: CLIMATE CHANGE IMPACTS AND VULNERABILITIES IN INDIA The intent of this annexure is to provide brief overview of climate change impacts on India and related vulnerabilities.10 India is one of the most vulnerable countries to climate change (Ministry of Environment and Forest, 2010). There has been a considerable amount of published literature on India’s climate change vulnerability and it is not in the scope of this report to dwell on such a volume of literature. However, a limited effort has been made here to make a point about India’s climate change vulnerability for the purpose of meeting the context of the report. India has been vulnerable to vagaries such as droughts, floods, heat waves and cyclones since time immemorial (BMTPC, 1997; High Powered Committee, 2002). These vagaries have left behind death and destruction which have had a huge impact on the developing economy of the country. India receives an annual average rainfall of around 975 mm, more than 75% of which is received in a span of four months from June to September. The performance of the Indian agrarian economy is very much dependent on these four months (Department of Agriculture and Cooperation, 2004). The primary vulnerability of the agriculture sector in India to climate change emerges from the impact of climate change on the onset, intensity, duration and cessation of monsoon rainfall and temperature patterns during the cropping season. The pattern of onset and withdrawal of the monsoon leaves northwest India with only a short rainy period while the 10
Most of the review presented in this annexure has been drawn heavily from Prabhakar and Shaw (2008).
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southwest and northeast parts of the country receive higher rainfall and a longer rainy season. Coupled with this, the short and intense rainfall spells make the dry land areas more vulnerable to runoff losses and prone to further drought. Around 68% of India’s cropped area receives rainfall of between 750– 2,000 mm per annum. These areas are highly prone to irregularities in monsoons such as late onset, long breaks and early withdrawal etc. and hence are vulnerable to droughts of different durations and magnitudes (Shaw et al., 2005). India has a long drought history. During the period 1871–2002, the country had 22 major drought years (years with rainfall less than one standard deviation below the mean). The Emergency Database (EM-DAT) of the Centre for Research on the Epidemiology of Disasters (CRED) reports the impact of drought in India. According to this database, droughts have affected nearly 1,061 million people and killed 4.25 million people in India during 1900–2006 (Center for Research on Epidemiology of Disasters, 2006). One of the major reasons for the droughts has been a strong link with the El Nino-Southern Oscillation (ENSO) patterns (Gadgil et al., 2003). For example, the country faced 10 drought years out of 22 during the ENSO period of 1965–87 while only 3 drought years during 1921–64 (Department of Agriculture and Cooperation, 2004). In recent decades, a weakening relationship between the Indian monsoon and the ENSO phenomenon was also suggested due to Eurasian warming. However, the recent droughts of 2002 and 2004 suggest the inherent vulnerability of the Indian monsoon system due to the El Nino phenomenon (Saith and Slingo, 2006). Selvaraju (2003) has clearly demonstrated the linkage between ENSO and Indian food grain production. Hence, it is clear that the Indian monsoon system, the dependent agricultural sector and droughts are very much linked to the regional and global climate system and hence are very much vulnerable to the changes in climate both at regional and global scales. Climate change is expected to change the existing vulnerability profile of India (Prabhakar and Shaw, 2008). The country level studies on the past climate indicated an increase in temperatures to the tune of 0.57 °C per 100 years. While some studies identified no national level trend in rainfall, there have been decadal departures above and below the long time average alternatively for three consecutive decades. At the regional scale, extreme 92
summer rainfall events have been observed in northwest India in recent decades. In addition, the number of rainy days during monsoons along the east coast has gone down during the last decade indicating more intense rainfall events. The computations of the extreme daily precipitation indices in India have depicted an upward trend in rainfall at 114 weather stations and a downward trend at 61 weather stations out of 130 stations across India. Extreme rainfall has significantly reduced in the Eastern part of the Gangetic Plains and part of Uttaranchal while the upward trend was observed from the Himalayas in Kashmir to most of the Deccan Plateau. There are few studies available on the possible future climate over Indian sub-continent. The climate projection studies indicated a general increase in temperatures in the order of 3-6°C over the baseperiod average, depending on the scenario, with more warming in the northern parts than the southern parts of the country (The Energy Resources Institute, 2001). Increased rainfall with increasing CO2 concentration was observed in experiments involving GCMs. The climate models predicted a change in precipitation by 5–25% over India by the end of the century with more reductions in the wintertime-rainfall than the summer monsoon leading to droughts during summer months (Lal et al., 2001). Studies by Lal et al. (2001) also suggested increased variability in the onset of monsoons with implications for sowing time for the farmers in future. Obtaining more specific regional projections has been difficult owing to lack of sufficient reliable regional models. Regional model studies involving HadRM2/HadCM2 indicated no substantial change in monsoon rainfall (Kumar, 2002). Climate projections developed for India for the 2050s using the regional model HadRM2 run on the IS92a emission scenario indicated an increase in average temperature by 2–4 °C during that period, an overall decrease in the number of rainy days by more than 15 days in western and central India, and an increase by 5–10 days near the foothills of the Himalayas and in northeast India. The projections also indicated an overall increase in the rainy day intensity by 1–4 mm/day except for small areas in northwest India where the rainfall intensities may decrease by 1 mm/day (Bhattacharya et al., 2006). 93
The high resolution (50×50 km) climate prediction experiments involving state-of-the-art regional climate modeling system called PRECIS (Providing regional climates for impacts studies) developed by the Hadley Center for Climate Prediction and Research revealed possibilities of increased rainfall and temperatures in global warming scenarios (Kumar et al., 2006). In these experiments, west central India showed maximum increase in rainfall with possibilities of extreme precipitations in Western Ghats and northwestern peninsular India. Projections using Soil and Water Assessment Tool (SWAT) water balance model and HadRM2 indicated droughts and floods in climate change scenario (Gosain et al., 2006). The studies indicated acute water scarcity conditions in the river basins of Luni, Mahi, Pennar Sabarmati and Tapi and severe flood conditions in the river basins of Godavari, Brahmani and Mahanadi. While considerable progress has been made in achieving the high-resolution climate predictions, the quantitative estimates still have large uncertainties associated with them (Kumar et al., 2006). Due to being far from conclusive and having a lack of dependable regional scenarios, it suffices to discern that governments at all levels need to be on watch for dealing with any surprises. Under these circumstances, identification of appropriate adaptation options seems to be the right approach. The right adaptation approach will be to look at the existing vulnerability reduction mechanisms and improve upon them by plugging the gaps.
References Bhattacharya, S., C. Sharma, R.C. Dhiman and A.P. Mitra (2006) Climate change and malaria in India. Current Science, 90(3):369–375. BMTPC (1997) Vulnerability atlas of India, Parts I–III. New Delhi, India: Building Materials and Technology Promotion Council. Center for Research on Epidemiology of Disasters (2006) India country profile of natural disasters. EM-DAT: The International Disaster Database. Available at http://www.emdat.net/disasters/Visualisation/profiles/countryprofile.p hp.
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Department of Agriculture and Cooperation (2004) Drought 2002: A Report. New Delhi, India: Department of Agriculture and Cooperation, Ministry of Agriculture. Gadgil, S., P.N. Vinayachandran and P.A. Francis (2003) Droughts of the Indian summer monsoon: role of clouds over the Indian Ocean. Current Science, 85(12): 1713– 1719. Gosain, A.K., S. Rao and D. Basuray (2006) Climate change impact assessment on hydrology of Indian river basins. Current Science, 90(3): 346–353. High Powered Committee (2002) Disaster management report. New Delhi, India: Department of Agriculture and Cooperation, Ministry of Agriculture, Government of India. Kumar, R.K. (2002) Regional climate scenarios, TERI workshop on climate change: Policy options for India, New Delhi, 5–6 Sept 2002. Kumar, R.K., A.K. Sahai, K.K. Kumar, S.K. Patwardhan, P.K. Mishra, J.V. Revadekar, K. Kamala and G.B. Pant (2006) High-resolution climate change scenarios for India for the 21st century. Current Science, 90(3):334–345. Lal, M., T. Nozawa, S. Emori, H. Harasawa, K, Takahashi, M. Kimoto, A. Abe-Ouchi, T. Nkajima, T. Takemura and A. Numaguti (2001) Future climate change: implications for Indian summer monsoon and its variability. Current Science, 81:1196–1207. Ministry of Environment and Forest (2010) Climate Change and India: A 4x4 Assessment. A Sectoral and Regional Analysis for 2030s. New Delhi, India: Ministry of Environment and Forests, Government of India. Prabhakar, S.V.R.K. and R. Shaw (2008) Climate change adaptation implications for drought risk mitigation: A perspective for India. Climatic Change, 88: 113-130. Saith, N. and J. Slingo (2006) The role of the Midden-Julian oscillation in the El Nino and Indian drought of 2002. International Journal of Climatology, 26:1361–1378. Selvaraju, R. (2003) Impact of El Nino-southern oscillation on Indian foodgrain production. International Journal of Climatology, 23:187–206. Shaw, R., S.V.R.K. Prabhakar and A. Fujieda (2005) Community level climate change adaptation and policy issues: a case study from Gujarat, India. Graduate School of Global Environmental Studies. Kyoto, Japan: Kyoto University. 95
The Energy Resources Institute (2001) India’s first national communication to UNFCCC. New Delhi, India: The Energy Resources Institute.
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ANNEXURE 2: IMPORTANT GLOSSARY OF T ERMS Term Adaptation Adaptive capacity Capacity
Climate change
Coping capacity
Disaster
Exposure
Mainstreaming Maladaptation Resilience Risk
Description Adjustment in natural or human systems in response to actual or expected climatic stimuli or their effects, which moderates harm or exploits beneficial opportunities. Property of a system to adjust its characteristics or behavior, in order to expand its coping range under existing climate variability, or future climate conditions. A combination of all the strengths and resources available within a community, society or organization that can reduce the level of risk, or the effects of a disaster. (Capacity may include physical, institutional, social or economic means as well as skilled personal or collective attributes such as leadership and management. Capacity may also be described as capability.) Change of climate which is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability observed over comparable time periods. Means by which people or organizations use available resources and abilities to face adverse consequences that could lead to a disaster. (In general, this involves managing resources, both in normal times as well as during crises or adverse conditions. The strengthening of coping capacities usually builds resilience to withstand the effects of natural and human-induced hazards.) Serious disruption of the functioning of a community or a society causing widespread human, material, economic or environmental losses which exceed the ability of the affected community or society to cope using its own resources Degree of climate stress upon a particular unit analysis; it may be represented as either long-term changes in climate conditions, or by changes in climate variability, including the magnitude and frequency of extreme events. Integration of adaptation objectives, strategies, policies, measures or operations such that they become part of the national and regional development policies, processes and budgets at all levels and stages Any changes in natural or human systems that inadvertently increase vulnerability to climatic stimuli; an adaptation that does not succeed in reducing vulnerability but increases it instead. Capacity of a system to tolerate disturbance without changing state or ability to bounce back after a disturbance or ability to tolerate the disturbance. Result of interaction of physically defined hazards with the properties of the exposed systems – i.e., their sensitivity or (social) vulnerability. Risk
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Term
Sensitivity
Vulnerability
Description can also be considered as the combination of an event, its likelihood and its consequences – i.e., risk equals the probability of climate hazard multiplied by a given system’s vulnerability. Degree to which a system will be affected by, or responsive to climate stimuli. Sensitivity is basically the biophysical effect of climate change; but sensitivity can be altered by socio-economic changes. For example, new crop varieties could be either more or less sensitive to climate change. Degree to which a system is susceptible to, or unable to cope with, adverse effects of climate change, including climate variability and extremes. Vulnerability is a function of the character, magnitude and rate of climate variation to which a system is exposed, its sensitivity and its adaptive capacity.
Source: From Appendix I: Glossary, p 869-883, IPCC (2007)11
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IPCC (2007) Climate Change 2007: Impacts, Adaptation and Vulnerability. In: M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson (eds.), Contribution of Working Group II to the Fourth Assessment Report of the IPCC, Cambridge, UK: Cambridge University Press, 976pp.
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ANNEXURE 3: COMMUNITY CONSULTATIONS FOR VETTING INDICATORS A focus group discussion (FGD) with the potential project beneficiaries was organized, in order to identify location specific indicators and to validate the indicators identified from the literature review, in the village of Kushjuri, Gram Panchayat Sonathali, Block Kashipur in the Purulia district of West Bengal (See Figure A-3, 1) in collaboration with the Executing Entity, Development Research Communication and Services Centre (DRCSC). The study location was chosen based on the criteria that the location is under consideration for inclusion in the project ‘Enhancing adaptive capacity and increasing resilience of small and marginal farmers in Purulia and Bankura districts of West Bengal’ submitted by DRCSC for the consideration of the Adaptation Fund Board which has already passed the first stages of screening. Located 47.1 km from the town of Bangkura, the Kashipur block consist of 13 Gram Panchayats with a total population of 186,980 and reports a sex ratio of 1:1.
S TEP I: D ISCUSSION
ON DEMOGRAPHIC BACKGROUND
The focus group discussion has started with the introduction of the purpose to the group by the DRCSC staff and discussion on the demographic and socio-economic conditions of the representing group. The group is made up of 22 male and 6 female participants representing agriculture cropping (83% male and 36% female), agriculture tool making (9% of males), handcrafts (64% of females) and share cropping (9% of males). Most participants were in the age group of 31-40 years (59% male and 50% female) followed by 20-30 years (27% male and 33% female respectively). In terms of the landholding size, the majority of male participants owned 2-3 acres (35%) and others owned less than an acre (30%). Majority of the female members 99
owned land less than an acre (67%) and others owned land in the range of 1-2 acres (33%).
Figure A-3, 1: Location of the village Kushjuri (red spot), Block Kashipur of Purulia District in West Bengal
Figure A-3, 2: FGD carried out in the village Kushjuri
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Figure A-3, 3: FGD participants and organizing staff of DRCSC
S TEP II: H AZARD
IDENTIFICATION AND PRIORITIZATION
Following discussions on demographics, talk then focused on understanding the past climatic hazards that are prominent in the location and to understand other overarching issues that are hindering the community wellbeing. Since adaptation projects are aimed to comprehensively address the vulnerabilities that are exacerbated by climate change, a broad based approach was adopted to identify the hazards and related issues. The following questions will help in understanding the nature of the discussion.
What natural climatic hazards has the region been facing? Please list and rank them in the order of impacts faced by the communities impacting their livelihoods and wellbeing.
During the discussion on hazard profile, communities also elaborated on the historical trends observed in the nature of hazards and their impacts. It was clear that certain locations may not have identifiable climate signatures. However, discussion on historical trends has enabled communities to understand the concept of change and to provide feedback on observed past changes in hazard and vulnerabilities. The following guide questions were used for historical analysis:
Have you observed any trends in climatic events in terms of number, duration, intensity and magnitude of the events? What have been the trends? What has been the nature of losses historically and how have they changed over the years? 101
A discussion was carried out on past hazards, trends in terms of intensity and duration, and time of onset or departure and impacts that communities faced, with a deeper discussion into impacts to identify factors that render communities impacted by past hazards. Participants in the group discussion identified the following climatic impacts including the changes that they observed (Table A-3, 1). The discussants believed that drought is the most important hazard followed by reduced rainfall (not necessarily leading to drought), warming temperatures, increasing fog and declining dew that is proving detrimental to agriculture operations and crop production. The table also presents broad trends observed by the discussants over the past several decades. Table A-3, 1: Major hazards and trends as identified by the communities and the ranking given in terms of their importance Hazards Storms Hail storm Frequency of fog Temperature Seasons Dew Rainfall
Lightening Droughts
Pest attack
Trends Intensity and number is increasing Getting erratic Increasing and erratic; previously it was only during winter Frequency, intensity and duration are increasing; leading to decline in drinking water availability Previously six seasons but now only three seasons Declining along with the soil moisture Untimely with increasing intensity destroying crops; there is no rainfall during field preparation time leading to increased irrigation requirement Increasing Increasing, 3 droughts in last five years; the trend has been observed for the last 10-12 years during which 6 droughts were experienced. Pest attack is increasing due to warming
Rank
4 3 (warming)
5 (decline) 2 (reduction)
1
The above observed hazards and trends are reportedly leading to growing health problems, reduction in household income due to multiple impacts of erratic weather and crop loss leading to growing migration in search of alternative livelihoods to the nearby cities and towns. There have also been instances of distress sale of livestock during drought periods due to lack of
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fodder and water for animals and health problems associated with hot weather conditions.
S TEP III: I DENTIFICATION
OF VULNERABILITY
INDICATORS
Discussion subsequently led to identification of vulnerability indicators in three categories. It followed the exposure, sensitivity and capacity model for identifying vulnerabilities. Communities were given an explanation about the meaning of exposure, sensitivity and capacity as below:
Exposure indicators: Exposure constitutes the degree of stress faced by communities, where stress includes mainly physical stressors such as frequency, magnitude and duration of hazards. Exposure also constitutes various elements that are present in an area where hazard events occur.12To what extent are communities exposed to hazards? Sensitivity indicators: Sensitivity determines the extent to which communities are affected by climate exposure. These factors make communities predisposed to impacts. What factors make communities sensitive to hazards? Capacity indicators: Indicators that enable capacities to cope and respond to stresses; and that will help communities to overcome the impacts. While some capacities can come to use instantaneously, realizing most capacities require an enabling environment which often takes time to realize.
To facilitate identifying vulnerability indicators, example indicators from literature review were shown to the discussants using flip charts. The ensuing discussion focused on elaborating the indicators based on the hazards and impacts identified and the example vulnerability indicators written on the charts. The following guide questions have helped in identifying the vulnerability indicators:
Exposure: For the purpose of VCA, climate exposure is obtained from published literature and hence the discussion on exposure
12
Cardona, O.D., M.K. van Aalst, J. Birkmann, M. Fordham, G. McGregor, R. Perez, R.S. Pulwarty, E.L.F. Schipper and B.T. Sinh (2012) Determinants of risk: exposure and vulnerability. In: Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor and P.M. Midgley (eds.), Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC), Cambridge, UK and New York, NY, USA: Cambridge University Press.
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was not conducted beyond a point of showing to communities a list of exposure indicators and obtaining their consensus. Sensitivity: Sensitivity indicators were vetted from the communities and the main purpose of the PRA exercise was to precisely identify different sensitivity elements that communities have and those will be exacerbated by future climate change. Due to paucity of time, sensitivity indicators were discussed for the categories of agriculture and food, water, land and biodiversity. This step was difficult for the participants to understand and contribute to. It took several explanations on the meaning of sensitivity leading to identification of additional indicators listed in the Table A-3, 6 with an astérisque (*). The following are some of the guide questions used for helping with the discussion: o What aspects of agriculture and food in the community are currently affected due to repeated droughts? o How dependent are community livelihoods on fresh water availability? o What additional indicators can we identify under this category in addition to the ones already listed on the chart? Can you identify any indicator that you think are not relevant? o Has there been any mortality of children under 5 year old due to water borne diseases? Is this an important indicator? Capacities: Here the emphasis is to list all the capacities that will help in overcoming the sensitivities that the discussants have identified above. The following guide questions have helped in identifying the capacities: o How did the community overcome water scarcity in agriculture? o What kind of livelihood diversification opportunities exist with communities? o What agriculture practices that farmers are practicing do they believe help them mitigate the impacts of climatic events13? o What practices do communities follow when there is scarcity of water or excess of water?
13
Droughts, floods, high and low temperatures, pest incidences related to temperature change etc.
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I NDICATORS A G RI C UL T U RE
IDENTIFIED :
AN D
FOOD
Among the agriculture and food indicators, the major sensitivity indicators identified were food insecurity which has been prevalent due to the prevailing drought conditions impacting agriculture production and economic wellbeing. Decline in green fodder sources and decline in traditional uncultivated food sources such as roots, tubers and fruits from forests that have been relied upon by communities for several centuries constituted the second and third most important sensitivity indicators (See Table A-3, 2). For example, Mahua, mushrooms, wild leafy vegetables etc. that have been collected for personal consumption, especially by the landless, and they serve as an important source of protein during both the normal and drought conditions, but they are reported to be on continuous decline. Currently, the region suffers from food insecurity for a minimum duration of 4 months in a drought year whereas previously it used to be 2 months. The other reasons cited for the growing food insecurity has been the lack of alternative cropping for the communities. Table A-3 2 presents the capacity factors that helped communities to overcome the sensitivities identified. Table A-3, 2: Sensitivity and capacity indicators for assessing vulnerability in agriculture and food Sensitivity Food insecurity Decline in traditional food sources Lack of distinct pre-kharif season Decrease in green-fodder sources Greater dependency on purchased fodder as the collection is going down Increasing expenses for food and fodder No more capture fisheries
Capacity Migration Public distribution system (PDS) and other food schemes for drought-prone areas Growing tuber crops in home gardens etc. Community grain reserves Mixed cropping and drought tolerant cropping (millets, Roselle etc.) Fodder tree planting around homestead Water harvesting Social forestry, to save the existing forests, needs to be promoted; introduce palatable tree species for animals into social forestry should be introduced
* These crops do not need much water and are disease and pest resistance; some tuber crops are collected from forests which serve as live reserves of starch and carbohydrates. Some Colocacia tuber species have a good amount of starch stored in them that can be used during drought periods. 105
Although several indicators appeared to have been identified in the above table (Table A-3, 2), they are only reflecting the need to restore traditional food sources for food security during drought periods. In addition, in terms of capacity indicators, the indicators from water such as water harvesting and social indicators such as migration have been identified which could be incorporated in respective categories, while indicators such as growing tuber crops in homestead gardens and drought tolerant mixed cropping reflect the need for diversification of food production practices. W A TE R Though several water-related sensitivity factors were identified (see Table A-3, 3), the significant ones are drying ponds, declining ground water levels and declining soil water retention capacity. The existing capacity to deal with these sensitivities include construction of small ponds and wells and cultivation of less water requiring crops and less water requiring practices appeared as important capacities. Table A-3, 3: Sensitivity and capacity indicators for assessing water related vulnerability Sensitivity Ponds are drying : Due to reduced rainfall, high demand due to paddy cultivation and high yielding varieties and vegetables and increase in water pollution Declining ground water level Fishes are dying Soil compaction is increasing and water retention capacity is reducing No crop (boro rice) in winter due to less water
Capacity Construction of small structures (e.g. ditches) and wells Cultivation of less water requiring crops Recycle of waste water from kitchen Cultivation of multiple use crops and perennial tree based crop SRI paddy Indigenous crops
L AN D In terms of land related indicators (Table A-3, 4), decline in soil micro flora and fauna, high runoff due to soil compaction and undulated land appeared as important sensitivity factors. The high runoff was due to less vegetation, dry and undulated land, and reduced soil water retention resulting in an increase in the number of irrigations for mustard crop d from 2 to 4. The existing capacities listed were knowhow on rainwater harvesting practices for water recharging, increasing land cover with mixed cropping and afforestation. 106
Table A-3, 4: Sensitivity and capacity indicators assessing land related vulnerability Sensitivity Declining soil micro flora and fauna Soil runoff Soil compaction in agriculture land Undulated land
Capacity More rainwater storage Water recharging Mixed cropping increasing the soil cover time with legumes Afforestation in highlands, bund across the slope and plantation on bunds
B I O - D I VE RS I TY Among the biodiversity indicators (Table A-3, 5), major indicators appeared to be decline in agro-biodiversity and decline in aquatic biodiversity. Decline in indigenous rice varieties has been prominent in the region; the number of cultivated crops has come down from a range of 10-15 to 4-5 in the past several decades. In paddy, the number of varieties has come down from 20 to 2 over the same period. In addition, wild vegetables from forests have also declined from three types of wild potato to one type that is available only in the month of September. Similar trends apply for leafy vegetables from forests. Among the tree species, there was a significant decline in the number of Madhuka indica trees, the flowers of which were used to mix with wheat flour for nourishing chapatti dough. This decline was observed due to population pressure, need for cash crops and division of families. In terms of aquatic biodiversity, communities have reported that there used to be quite a variety in the aquatic food species available several decades ago. Today, only few species of fish are available and breeding of fish is hampered by a decline in fish diversity. The communities have also reported a decline in number and diversity of animals that they are used for food. They have reported that only wild boar is seen now in the wild, as a result of which the festivals that are based on hunting have also gradually vanished. Among capacities, practices such as community forestry programs, breeding indigenous varieties and organic farming were identified. Community forestry programs were reported to be able to help only to a limited extent. Although new species of animals and birds were observed to take shelter in the community forests, the sensitivities listed in Table A-3, 5 were not helped by the community forests; however, the animals and community forests 107
are found to be helping in soil fertilization, increasing the aesthetic sense, normalization of temperatures etc. Reportedly, the declining biodiversity has made communities shift from hunting and gathering lifestyle to animal husbandry. Table A-3, 5: Sensitivity and capacity indicators for assessing the biodiversity related vulnerability Sensitivity
Capacity
Declining agro-biodiversity Decline in aquatic biodiversity Reduction of animals for hunting
F INAL
Community forestry programs Breeding indigenous varieties Organic farming
LIST OF INDICATORS AFTER THE FIELD VISIT
After conducting the focus group discussion with communities, the identified indicators were revisited for the commonality and overlaps among them with the indicators from literature review. A short list of indicators was then incorporated into the set of indicators that were originally taken to communities for their consultation (Table A-3, 6). The indicators with asterisk are the new indicators obtained from the community consultation process.
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Table A-3, 6: List of indicators for community consultations (asterisk ones were updated from FGD in Kashipur) Component Indicators Vulnerability indicators Water Projected change in precipitation (Exposure) Projected change in temperature (Exposure) Internal and external freshwater dependency (Sensitivity)* Water scarcity (Sensitivity)* Population with access to improved water supply (Capacity)* Mortality among under 5 yr.-olds due to waterborne diseases (sensitivity) Population with access to improved sanitation (Capacity) Agriculture Projected change in agricultural (cereal) yield (Exposure) and food Coefficient of variation in cereal crop yields (sensitivity) Population living in rural areas (Sensitivity) Dependency on market for fodder and food (Sensitivity)* Projected change in traditional sources of food (Exposure)* Area under improved food production practices (Capacity)* Food insecurity (Sensitivity)* Agricultural Capacity including indigenous knowledge (Capacity)* Children under 5 suffering from malnutrition (sensitivity) Biodiversity Projected change in biodiversity due to climate change (Exposure)* Capacity for biodiversity conservation (Capacity)* Declining biodiversity (sensitivity)* Health Estimated impact of future climate change on deaths from disease (Exposure) Mortality due to communicable (infectious) diseases (sensitivity) Health workers per capita (Capacity) Health expenditure derived from external resources (sensitivity) Longevity (capacity) Maternal mortality (sensitivity) Land Land less than 10 m above sea-level (sensitivity) Area under improved land management practices (Capacity)* Area of land flooded/drought prone per unit area (sensitivity)* Percentage of degraded land (sensitivity)* Population living more than 10 m above sea-level (capacity) Infrastructure: Population with access to reliable electricity (capacity) Energy Energy sources at risk (sensitivity) Infrastructure: Roads paved/access to markets (Capacity) transport Amount of roads susceptible to flood damage (sensitivity) Readiness indicators Economic
Governance
Social
Choice for marketing products and services Migration Livelihood choices Access to credit Voice & Accountability Political Stability & Non-Violence Control of corruption Mobile telephones per 100 persons Tertiary Education Rule of Law
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ANNEXURE 4: LIST OF PROXY INDICATORS S Indicators in VCAI No 1 Readiness to migrate 2 Access to markets 3 Alternative livelihoods
4 Access to credit 5 Access to lifelines (health, education, water, transportation, communication) 6 Political stability and violence 7 Extension services 8 Rural development programs 9 Physical assets that support diversification of livelihoods 10 Drainage facilities 11 Social groups (SHGs, Water user associations, cooperatives etc.) and networks 12 Access to CCA technologies 13 Longevity 14 Crop diversification
15 Management capacity (for better water management techniques)
Suggested Proxy Indicators14 % of population that migrated in a previous disaster Number of markets within the 5km radius Distance to nearest market Size of the market Number of employment options available other than agriculture livelihood opportunities that are not directly impacted by climate Number of banks that lend credit to the subjects in question (e.g. lending to farmers, landless, poor etc.) Number of healthcare facilities Number of tube wells for drinking water Number of telephones Road condition and type Number of violent events that occurred in the last one year Number of extension workers per 1000 people Number of programs by government and other agencies that cater to rural development Number of boats Number of fishing nets Number of tractors Length of drainage canals Distance to nearest drainage canal Number of water user associations Number of cooperatives Enrollment in cooperatives Number of technologies that are being promoted for climate change adaptation Years Number of types of crops grown per 1000 ha or in a village Number of sources of protein Number of sources of carbohydrates Number of people trained in better water management techniques
14
These are indicative only. Units could be different. For e.g. number of markets per 1000 could be converted from units such as per 10,000 or per district or Zillah Parishad etc.
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S Indicators in VCAI No 16 Catch potential 17 Capacity for biodiversity conservation 18 Landscape condition 19 Species richness 20 Keystone species
21 Habitat loss 22 Food insecurity
23 Dependency on market for food or fodder 24 Access to natural resources
Suggested Proxy Indicators14 Yield (tons) Number of forest conservators trained in biodiversity conservation Number of programs implemented for biodiversity conservation Area of land that is degraded Area of land that is denuded Area of land that is undulated Number of species Number of keystone species Number of predators Number of tree and flowering plant species that withstand severe droughts Area of forest degraded Percent of households/people suffering from malnutrition Percent of people/households that ran out of food often Percent of people/households that ate poor quality and or insufficient quantity of food Percent of food that is purchased from market Percent of fodder that is purchased Percent or amount of forest usufructs obtained for livelihoods Ownership of land (ha, per capita) Percent of water obtained from public sources such as ponds etc. as a primary source
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ANNEXURE 5: LIST OF INDICATORS FOR POLICY AND INSTITUTIONAL ASPECTS The VCAI methodology was primarily developed to conduct vulnerability assessments (VCA) at the project level. However, it should be recognized that project performance is not independent of the institutional and policy environment of a country and states within which the project is situated. Hence, depending on the scale of the project that is being implemented, it becomes necessary to consider the influence of larger policy and institutional contexts. An effort has been made to provide an indicative list of indicators that can help the EEs to include the same in the VCAs. The VCAI methodology is flexible enough to accommodate new indicators. EEs can modify the Excel tool associated with this documentation to include the additional indicators they deem fit. Only sensitivity and capacity indicators were provided since the exposure indicators are same as those included under different sectors in the VCAI tool. It has to be noted that most of the institutional and policy and program indicators tend to be qualitative in nature and often are not reported in the regular databases of the governments and other sources. Therefore it may pose a challenge to use them in the VCAI tool which is constructed largely by the quantitative indicators.
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Table A-5, 1: Vulnerability indicators for policy and institutional dimensions Level Policy and programs
Institutional
Sensitivity Policies that enhance inequality Poor financial allocation to adaptation (e.g. ratio of finances allocated to nonadaptation programs) Lack of knowledge and recognition for CCA among policy makers Policies that do not consider climate change impacts Lack of policy coordination/ad hoc (e.g. represented by number similar programs run by different ministries)
Lack of financial, human and technical resources (e.g. reflected by number of projects, ratio of resources allocated to nonadaptation projects etc.) Lack of knowledge/skills on CCA (e.g. proportion of untrained staff )
Capacity Adaptation plans at national and sub-national levels Presence of national strategy for CCA Level of mainstreaming of CCA into development plans and policies Poverty reduction programs to assist vulnerable communities Financial risk management programs including insurance Special funds (e.g. Adaptation Funds, Disaster Management Funds, Relief Funds) Presence of employment guarantee programs Special funds (e.g. CCA, DRR and relief funds) Risk spreading instruments and credit facilities for the poor Plans for including representatives of women’s interests Alignment with gender policies and gender commitments (e.g. National Policy on Empowerment of Women, 2001) Proportion of budget allocated to gendersensitive development programs Use of climate information, risk and vulnerability assessments in policy and program formulations Capacity building programs for CCA Number of staff trained on risk and vulnerability assessments The level of mainstreaming of CCA into institutional aspects Plans for gender-sensitive redress mechanisms Gender mainstreaming focal point identified Number of staff trained on adaptation planning, gender neutrality Presence of financial risk management plans Networking of institutions for sharing information Adoption of participatory approaches Women participation in decision making Availability of data for risk and vulnerability assessments Amount of funds invested in capacity building Proportion of institutions engaged in CCA and DRR
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ANNEXURE 6: STATISTICAL SAMPLE SIZE Determining statistical sample size can be done by using the procedure provided in this annexure. It is suggested that the ssurveys are to be conducted with a sample of community members representative of the unit of analysis i.e. a village, Panchayat, Zilla Parishad. Specific number questionnaire surveys or focus group discussions (FGDs) could be done to collect data on specific indicators for which published data is not available. The participants of the survey or FGDs could be farmers, vulnerable groups including women, children, old, poor, indigenous groups etc. The sample size can be determined using the equation:
Sample size (n) =
t 2 p(1 p) m2
………………………………………………………….Equation 6 Where t= confidence interval (usually taken 1.96 for 95% of confidence level) p= estimated prevalence (number of particular group of participants i.e. women, poor etc. in the population being surveyed) m= Margin of error (usually given at 5% or 0.05) The sample size obtained by using this formula has to be adjusted for the return rate of the questionnaires, possible errors in filling the questionnaires etc. Please take particular note of the budgetary implications of following the statistically determined sample size.
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ANNEXURE 7: PRA TECHNIQUES FOR VCAI 1. Communication maps: Participatory tools that can assist in understanding communication patterns and relationships among the communities and other actors engaged in communication of information. These maps will help in understanding how an actor communicates with others, what they communicate and how much importance they assign to that communication. Communication maps often depict the person in view at the center with arrows showing the connections the person has with other actors within the communication channels he/she established.15 Suggested questions: Who are the actors engaged in communication, what do they communicate, how they communicate, how far are they from each other? 2. Cross impacts analysis: This participatory tool was widely engaged in the Delphi method of expert consultations wherein the Delphi panel members are required to take into consideration the possible outcome of a set of forecasts interacting with each other, even though the interactions were not taken into consideration while developing the forecasts. While there are several techniques engaged in cross impact analysis, the primary purpose of this tool is to reduce the uncertainty in the future both in the scenario development and the use of the same in policy applications especially for the purpose of CCA, as the tool helps to gain insights into the future development.16 Suggested questions: what are possible future scenarios, how may uncertainties impact on achieving these scenarios, what possible interventions could help avoid the obstacles? 3. Focus group discussions: Abbreviated to FGDs, these are employed widely in most appraisal exercises as they are versatile to integrate other PRA tools described here. FGDs consist of group of discussants, either randomly drawn from
15
Zaveri, S. (2009) Tips for trainers. Communication maps: A participatory tool to understand communication patterns and relationships. Participatory Learning and Action: Community-based Adaptation to Climate, 60: 180-182. 16 European Commission (2008) Online foresight guide. Available at http://forlearn.jrc.ec.europa.eu/guide/2_design/meth_cross-impact-analysis.htm.
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the study site or stratified according to the objective of the exercise, discussing on a given issue at length with the help of a moderator who keeps the discussion focused in order to achieve the set objective. FGD setup can integrate other tools such as mapping, ranking, Venn diagrams, livelihood matrix etc. The success of the FGD depends on how well the purpose is defined and how the discussions are moderated. Proper understanding is necessary on the social dynamics within the group that could hinder the equal participation and expression of opinions during the discussion. Suggested questions: Depends on the purpose of the FGD. What are the important hazards in the village, how sensitive are the livelihoods to changes in climate, what resources do communities have access to use during the stress periods, what are the coping strategies? 4. Gender audit: Gender audit is a comprehensive participatory methodology for the organizations and other entities to conduct thorough participatory audit of how far the organization has achieved the gender mainstreaming, achieving gender neutrality and understanding gender sensitivity. Dealing with the method in detail is beyond the scope of this document and hence it is suggested to refer to detailed sources which can give a good understanding on the methodology including that of ILO manual for gender audit facilitators, namely, the ILO participatory gender audit methodology (2007).17 In brief, the gender audit consists of four pillars which are implemented using participatory workshops that encourage greater interaction among the participants with an ultimate goal of achieving gender equality. It should be noted that the gender audit involves multiple steps often consuming time and resources with several workshops, desk reviews and meetings to facilitate information exchange, issue prioritization and solution development. Suggested questions: Why are things the way they are, what equal opportunity means, what is the role of different members of the society in achieving the equality? 5. Gender analysis: Women’s groups etc. are considered to be one of the most vulnerable groups to changes in climate as they tend to have the least power in society while still having
17
ILO (2007). Available at http://www.ilo.org/dyn/gender/docs/RES/536/F932374742/web%20gender%20manu al.pdf.
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to carry out household and income generation tasks. In order to understand the factors that predispose women to greater stress, it is suggested that a thorough gender analysis is carried out by all EEs before the project is implemented. Many tools described in this section could be used for conducting gender analysis including power mapping, focused group discussions, developing access and control profiles, sex-disaggregated Venn diagrams, mapping gender roles, seasonal calendar, problem tree, cash inflow and cash outflow calendar, gender budgeting, gender audit etc. Suggested questions: What are the gender sensitivity issues in the village, who takes major decisions in the family, who has greater access to productive assets, who plans the expenditure in the household? 6. Hazard impact on livelihood matrix: Livelihood matrix consists of a participatory exercise where the participants evaluate the list of livelihood options available at their disposal against a set criteria. Usually, the process starts with drawing up a table on a sheet of paper with the first column showing the list of livelihood options available in a given location and the rest of the columns for weighing these livelihood options against a set of criteria. The livelihood option that is ranked high against most criteria is chosen as the most robust livelihood option. In case of hazard impact livelihood matrix, one of the criteria will include hazard impacts on livelihoods and the livelihood that is least impacted by hazards will receive high ranking. Sometimes, this method is also referred to as livelihood sensitivity matrix.18 Suggested questions: What are the livelihoods available to the communities, what are the challenges in benefiting from these livelihoods, what criteria determines the success of these livelihoods, how can various livelihoods be weighed against the set criteria? 7. Hazard mapping: Provides opportunity for the participants to consider the spatial spread of various hazards and risks within a given locality and to design interventions. These maps could be used to understand the risks and vulnerabilities for proper planning. For the purpose of 18
The World Bank (2007) Tools for Institutional, Political, and Social Analysis of Policy Reform: A Sourcebook for Development Practitioners. Washington D.C.: The World Bank.
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accuracy, once the maps have been developed in a participatory manner, they could be validated using the information from well recognized sources such as meteorological agencies. Depending on the availability, mapping can be done using a range of approaches consisting of sophisticated GIS tools to using paper or on the ground and subsequent transfer to a paper based medium for archival. Suggested questions: what are the hazards in a given location, how are they located in a given village, who are affected by these hazards, what are the vulnerabilities and capacities? 8. Hazard trend analysis: While hazard mapping provides spatial understanding of hazards and risks in a given location, hazard trend analysis helps participants to understand hazards over time. It consists of discussion on historical trends in hazards in a given location. In general, participants discuss the change in type, magnitude, time of occurrence, duration, intensity and change in nature and severity of impacts. The exercise will help participants to understand well the changes happening in a given location and appreciate the need to design interventions and to anticipate the future changes. One major limitation with the hazard trend analysis is that it relies upon the memory of the participants and hence validation of the information generated against published sources is strongly advised. Suggested questions: What are the changes in types of hazards, what changes were observed in the intensity of hazards, what vulnerabilities have exacerbated the impacts of hazards? 9. Mental models: Mental models are the internal representations of the external reality that people develop based on their own perceptions of the reality. Understanding the mental models is important in shaping perceptions and initiating actions among the actors. Mental models are particularly important in climate change for the reason that climate change is a complex issue and there may be limited understanding on the issue. Mental models are developed by facilitating the participants to draw, pictorially represent, various driving forces, pressures, states, impacts and responses to climate change. Mental models will help community members to understand the causes and identify appropriate adaptation interventions and help clarify myths and misunderstandings associated with climate change. 118
Suggested questions: What do you understand about climate change, what is the urgency in adapting to climate change, what is causing the communities vulnerable to climate change impacts, what approaches can help reduce the vulnerabilities? 10. Participatory scenario development: Climate change is riddled with uncertainties in terms of projected impacts and efficacy of actions introduced to address the impacts and scenarios could help address these uncertainties to a greater extent. Participatory scenario development will help participants to develop descriptions of how the future may look based on the currently available information and based on a set of assumptions and how adaptation interventions will work and help develop understanding on how adaptation interventions could help shape the future plausible development. Participatory scenario development can help extent the available scenarios, help apply the scenarios for the practical purposes, help as a capacity building tool and avoid failure of interventions in a long-time scale that is especially essential in climate change adaptation? Suggested questions: How might the future look, what determines future development, how may vulnerabilities change in the future and what adaptation options may reduce the future vulnerabilities? 11. Power mapping: Community vulnerabilities are exacerbated if access to natural resources is hindered due to power structures that operate within the community and this can act as a sensitivity factor for the vulnerable. Understanding the power structures within the community is also important for the reason that women and children are most affected by changes in the climate as they tend to hold insignificant power within the community though they carry out most jobs such as collecting firewood, water and food for the family. Power mapping is also useful to understand the relative power distribution among social and economic groups within a village. Power mapping consists of drawing up a table (or using a Venn diagram) showing environmental resources and benefits within the village in the first column and subsequent columns showing the access and control rankings for various groups such as women, children, men, poor, rich etc. The participants can be asked to rank who has the most power in accessing and controlling these resources, for example on a scale of 1-5. 119
Suggested questions: what resources exist within the village, who are the users of these resources, what rating can be given to women in terms of access to land, what rating can be given to the poor for control of resources and benefits? 12. Problem/Preference ranking: Problem/Preference ranking consist of ranking of preferences that community members have in order to address a given problem. Often, such ranking exercises consist of asking the community members to list the existing problems and preferences and to rank them according to the importance they place on each problem or preference being considered. Ranking exercise could be simple ranking or it can be pairwise ranking wherein the respondents are asked to choose the bigger problem or better preference out of every pair of problem or preference and identifying a problem or preference that received highest number of preference. This method helps to understand the community common problems and preferences, reach a consensus on which preference need to be given highest priority and to help assign resources to implement the most preferred adaptation option. Suggested questions: What are the problems in a given location, what are your preferences for solving the problems, what solutions need the most resources, what solutions can be easily implemented? 13. Rain calendars: These are calendars produced in a participatory approach for understanding the changes in rainfall such as changes in intensity of rainfall, duration of rainfall, length of dry spells, date of onset and date of withdrawal of rainfall etc. Combined with cropping patterns, livelihoods and other economic activities, rain calendars can help communities understand the impact of changes in rainfall on the associated livelihoods and local economy. Suggested questions: In what month does the rains start, what is the duration of rainy season, when are the probable breaks in the rains, what are the impacts of failure of rains? 14. Ranking: Ranking consists of participants enlisting the issues and assigning rank according to the importance assigned by the participants to an issue or solution being ranked. Ranking helps communities to prioritize issues and solutions, reach a consensus on possible allocation of resources for problem solving. Ranking is often done by voting by the participants. When exercise is done among multiple groups, top priority issues can be identified either by calculating and comparing the frequency with which a 120
certain issue has come up in the discussion or by calculating the average rank given to an issue across the groups. Suggested questions: What are the adaptation options available, which of the options are effective, which options are costly, which options are cost effective? 15. Resource maps: Resource maps are the maps that depict the spatial spread of resources that communities possess and the purpose for which communities use these resources. Drawing resource maps helps assess the capacities that communities have in addressing the hazards and risks and hence forms an important tool in vulnerability assessments. The process of drawing resource maps can help understand issues in accessing the resources due to social and ownership issues and hence the true efficacy of these resources in minimizing the impacts of hazards. Resource maps have been employed in wide variety of fields including in forestry programs, agriculture, rural development, CCA and DRR. Suggested questions: What resources do communities possess in the village, how accessible are these resources to various social groups, where are they located and how are they used during natural disasters? 16. Seasonal calendar: Seasonal calendars are important participatory tools employed to understand the seasonal spread of livelihood and other activities in which communities are engaged. Seasonal calendars are drawn by participants by identifying the months in a year and the livelihood activities carried out in each month. Seasonal calendars are very versatile tools and hence can help understand the patterns associated with issues such as employment, food scarcity, rainfall, cropping, fodder availability, water availability and occurrence of diseases. When seasonal calendar is made for crops, it is called as cropping calendar. Drawing seasonal calendars will help understand lean periods, what best time is for implementing interventions and for finding opportunities for alternative livelihoods and other income generating activities. When combined with historical trend analysis, seasonal calendars can help in understanding the long-term patterns in the elements assessed. Suggested questions: What is the most busy time, when is the employment potential high, when are the crops cultivated, when is the fodder least available? 17. Social maps: Social maps consist of a map drawn by the participants depicting the social structures, institutions and 121
households present in a given area. Often, social maps are used to monitor the progress made by a project in terms of its impact on socio-economic conditions of the households. In such a case, households are ranked in relation to others and their status is indicated in terms of a given socio-economic conditions such as access to sanitation. These base maps are then used to monitor the progress at regular intervals during the project implementation. Social maps can help understand different social institutions present in an area, how communities interact with these institutions and their opinion on the social services they provide. Suggested questions: What are the social groups in the village, what are the socio-economic conditions of these social groups, how do social structures in the village influences their vulnerabilities? 18. Transect walks: Transect walks consist of walking across a village or a given area to obtain preliminary understanding about the study areas in terms of physical placement of different social groups, features, landscape, resources, hazards and risks along the transect. Transect walks are undertaken at the first instance of entering into the study area to gain firsthand information about the cause and effect relationships among natural features and the human settlements. Transect walks are planned by drawing transect on a locality map or a community resource map identified with the help of local participants often covering the major variations in the bio-physical and socio-economic features of the area. Suggested questions: Where are the resources located in the village, what transect can cover the most prevailing variations in topography, socio-economic conditions and assets located in the village, what disparities exist in the village? 19. Venn diagrams: Venn diagrams show the overlaps and relationships between institutions, groups and organizations found in a village and the opinion of villagers on these organizations. Venn diagrams help understand the institutional disparities and capacities that exist in a village. In an institutional Venn diagram, the institutions are depicted as circles and the size of the circle shows the relative importance communities assign to these institutions. Here, village or communities are shown as the largest circle and other circles are placed in relation to the big circle keeping in view the relative importance, interaction and distance from 122
the communities. Institutions that do not have close contact with the communities are shown farther from the community circle. Suggested questions: what are the institutions present in a village, how close are these institutions to the communities, what is the importance of these institutions to communities, what services do they provide and what role do they play for climate change adaptation? 20. Vulnerability and capacity matrix: Consists of a table with the first column showing the physical, social and motivational factors and the other two columns showing the vulnerabilities and capacities. While preparing the matrix, the respondents are asked to identify the physical vulnerabilities that the communities have and the physical assets and other capacities that they have in helping reducing the impacts of the hazards. Similarly, the facilitator will ask the participants to identify social and organizational vulnerabilities and capacities and motivational and attitudinal vulnerabilities and capacities. Though the process is simple, this tool has important application in participatory vulnerability assessments and hence has been widely employed. The process will help participants understand how various vulnerabilities and capacities relate to each other and within the sectors that are important for the wellbeing of the village. Suggested questions: What are the physical, social and motivational vulnerabilities, what are the physical, social and attitudinal capacities, how are these vulnerabilities and capacities grouped and associated with various economic sectors? 21. Wealth ranking: Wealth ranking consists of asking the participants to rank the households in a given village according to the wealth they possesses. Wealth ranking helps in understanding the socioeconomic differences among different wealth groups, helps in identifying associated inequalities, how communities perceive wealth and impact of wealth on the ownership and access to natural resources and other productive assets present in a village. Wealth ranking is one of the most important but yet contentious tool employed in participatory rural appraisals. While understanding that wealth is an important aspect of vulnerability assessments, people often are hesitant to disclose the wealth in a group process because of the sensitivities they have. There are other limitations such as inability to resolve beyond the household 123
level and can pose the hazard of providing a static picture of a dynamic poverty issue. Suggested questions: What socioeconomic groups exist, how does their wealth compare with each other, how does the wealth influence access to resources and productive assets?
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ANNEXURE 8: ADAPTATION DECISION MAKING USING MULTI-CRITERIA METHODOLOGIES A8.1 I NTRODUCTION From the vulnerability indicators identified in this report, it is evident that there could be several vulnerabilities that various stakeholders would like to address through adaptation interventions. However, resources are limited and only a limited number of vulnerabilities could effectively and efficiently be addressed within the time and resource framework in which a project operates. Hence, in this scenario, stakeholders prefer to prioritize adaptation interventions that could address most vulnerabilities in a cost effective manner. Here, the criteria to identify prioritize adaptation interventions is being able to address most vulnerabilities and being cost effective while addressing maximum extent of vulnerabilities. Similarly, the criteria to choose adaptation interventions could vary from stakeholders to stakeholders and from socio-economic and vulnerability contexts. In order to reconcile the multiple criteria involved in picking appropriate adaptation option, the methodologies termed as multi-criteria analysis (MCA) have been widely suggested. Table A-8, 1 provides a list of methodologies that have been put to use to reconcile situations where two or more criteria and alternatives (or interventions) are involved. Among several MCA methodologies, Analytical
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Hierarchy Process (AHP) class of methodologies stand out for the advantages they provide as shown in Table A-8, 2.19 Table A-8, 1: Pros and cons of methodologies employed for adaptation decision making (Ilori and Prabhakar, 2014) Method Cost-benefit analysis
Pros Allows comparison between sectors Provides project specific assessment Proven economic tool Easy quantitative comparison across alternative adaptation options
Cost-effectiveness analysis (CEA)
Provides budget estimate Could provide economy-wide policy assessment
Multi-criteria analysis (MCA)
Good to compare costs of adaptation across regions with similar circumstances and objectives Good to provide indicative comparison of national adaptation costs with national mitigation costs (worked out from different models) More criteria possible Participatory approach Proven modelling concept Broader approach and could include economic social, environmental, technical and financial criteria Could rank different adaptation options on considering multiple criteria Could generate environmental and social indicators
19
Cons Heavy on quantitative data Extensive data and analysis Difficult to get cost and benefit data for social parameters. Generally performed from a project/policy-perspective and not from user (e.g community needing adaptation measures) perspective Provides ranking only Requires macro-level assumptions which could be distant from microlevel adaptation needs and realities Arriving at a common discount rate for different communities could be tricky Requires extensive data and analysis Defining objective function could become subjective for adaptation policy Manipulation easy Provides ranking only Needs trained human resources Requires extensive data and analysis Defining multiple criteria and preferences for policy outcomes could become subjective for adaptation policy Requires macro-level assumptions, which could be distant from micro level adaptation needs and realities
Most of the discussion in this Annexure has been drawn from the source Prabhakar, S.V.R.K. (2014) Adaptation decision making frameworks and tools: Multi-criteria decision making tools for prioritizing adaptation actions at community level. IGES Research Report No.2013–02. Hayama, Japan: Institute for Global Environmental Strategies.
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Table A-8, 2: Examples of MCA techniques (Modified from Malczewski et al., 1997) MCA technique Analytic Hierarchy Process (AHP)
Goal programming PROMETHEE
Strength
Weakness
Most reliable MCA method. Easy to interpret. Efficient for project and policy evaluation. Intuitive and flexible over other methods. Helps evaluates measures and alternatives. Helps capturing both subjective and objective evaluation measures and alternatives. Pair-wise comparison is easy to understand. Group decision is supported through consensus by calculating geometric mean of the individual pair-wise comparisons. Reduces bias in decision-making. Offers effective means in situations of uncertainty and risk through derivation of scale where measures do not exist. Simple and easy to use. Handles large number of variables, constraints and objectives. Provides a complete ranking from best to worst. Unlike in AHP, loss of important information which occurs through aggregation does not occur.
Irregularities can occur in ranking. Compensation between good scores on some criteria and bad scores on other criteria can occur. Pair-wise comparison may become so large (n(n-1)/2) that it becomes a lengthy task. Difficult to implement with many criteria.
Use of software may be difficult to understand. It is complicated as it involves three steps- the PROMETHEE 1, the PROMETHEE II and the GAIA (Geometrical Analysis for Interactive Aid) plane. Different types of farming techniques. It does not provide decomposition of problem and building of hierarchy. Evaluation becomes possible when criteria are more than seven. No specific guidelines to determine weight
A8.2 T HE A NALYTIC H IERARCHY P ROCESS (AHP) The Analytic Hierarchy Process (AHP) is one of the widely used MCA techniques (Teknomo, 2006). It was developed by Saaty (1990) and has been applied to situations that involve decisionmaking in both the private and public sector. It is very straightforward and comprehensive, making the decision evaluation easy to communicate to relevant stakeholders. The AHP models a decision making problem and allows the inclusion of tangible and intangible objects (Mu, 2005). The top element of the hierarchy is the goal for the decision model (Figure A-8, 1). This makes possible the structuring of a multi-dimensional problem into a hierarchical tree with criteria and alternatives. Opinion is extracted during the evaluation process using pair127
wise comparisons. In a simple term, AHP process is an approach to decision-making that involves structuring multiple choice criteria into hierarchy, assessing the relative importance of these criteria, comparing alternatives for each criterion and determining the overall ranking of the alternatives (DSS Glossary, 2010). By organizing and assessing alternatives against a hierarchy of multifaceted objectives, AHP provides a proven, effective means to deal with complex decision making. AHP offers an avenue to efficiently identify and select criteria and provide weight.
Figure A-8, 1: Decision hierarchy (Ilori and Prabhakar, 2014) A P PL I C A TI O N
OF
AHP
I N S E L E C TI N G AL TE RN A TI V E S
AHP has been widely applied in the literature. While some of its advantages are extensively discussed in the literature (e.g. Vreeker et al., 2002), Yin et al. (2007) employed it in evaluating adaptation options for the water sector in the Heihe River basin of north-western China to make judgments about how effective different options are with respect to four decision criteria and to determine the relative importance of the selected criteria. The criteria selected for the study include water use efficiency, economic returns to water use, environmental effects and cost. From the results, intuitional options were ranked above engineering measures to increase water supply. Options that were preferred include economic reforms and water consumer. In Mongolia, herders, scientific experts and authorities from local, provincial and national offices were asked to participate in evaluating adaptation options for livestock sector (Batima et al., 2005). Options that promote adaptation and developmental 128
goals, consistency with government policies and environmental impacts were screened against some selected criteria. The options that were selected in the initial screening were then evaluated against a second choice of six additional criteria – capacity to implement, importance of climate as a source of risk, near term benefits, long-term benefits, cost and barriers. Adaptation strategies that were chosen as priorities are measures that have general near- term benefits by improving capabilities for reducing the impacts of droughts and harsh winters as well as measures that produce long-term benefits through improving and sustaining pasture yields. Recommendations were made that there should be improved pasture management through traditional system of seasonal movement of herds, animals’ winter survival capacity should be increased by modifying grazing schedules and there should be an increase in the use of supplemental feeds. In all these examples, AHP was able to provide useful tools in prioritizing adaptation options displaying its robustness and relevance for employing it in adaptation decision making.
A8.3 C ONDUCTING FGD
USING
AHP
The aim of FGDs would be to facilitate and encourage EE staff/participants identify indicators, criteria and practices on their own with minimum suggestive inputs from the facilitators as much as possible. The flow of the process, which is for conducting FGD only for the prioritization of adaptation practices, is show in Figure A-8, 2. Reference should be made to Figure 18 under sub-section ‘What is next after vulnerability assessment?’ where an integrated approach to combine both the vulnerability assessment and prioritization of adaptation practices is shown. The role of the facilitator in the whole process is to ensure that the participants understand the purpose of the exercise and facilitate the discussion leading to prioritization of adaptation practices. Facilitators are discouraged from carrying with them any predetermined set of indicators/criteria/ practices with them but rather to identify them bottom up. The group discussion could consist of two phases: 1. Phase I, Background process: Explain the objective of the meeting, explain the concepts involved including vulnerability, adaptive capacity, exposure, adaptation 129
practices, effectiveness indicators and criteria. Facilitate listing past climate related events, their social, economic and environmental impacts, which practices did well and which practices did not do well. Identify and rank practices for the impacts they have experienced, discuss why those practices were enlisted based on what indicators the respondents had in their mind while ranking the practices, criteria that the group had in mind while listing the indicators. By end of the discussion, the group should have enlisted and ranked practices, indicators and criteria. 2. Phase II, Pairwise comparisons within each subgroup: the group will go through the exercise of pairwise comparison of criteria, indicators and practices as described below: a. Pairwise comparison of criteria b. Pairwise comparison of indicators per each criteria c. Pairwise comparison of practices per each indicator 3. The pairwise comparisons should be done using Saaty’s fundamental scale of judgment (See Table A-8, 3, Saaty’s scale of judgment). The pairwise comparisons should be done by asking the respondent to choose one level among 9 levels of strength that the respondent feels the criteria/indicators/practice are related to each other in contributing to the superior objective. The discussion for pairwise comparison could be done by asking the respondents the question ‘which of these two practices will satisfy the indicator x’. 4. After identifying an indicator among the pair of indicators being compared, the respondents can be asked ‘how much this practice satisfies the indicator in question compared to other indicator’. At this stage, the group could be shown the Saaty’s fundamental scale of judgement written on a chart for their reference. It would be easy if pairwise comparisons are written on charts in advance to save time during the FGD. The Photo below shows a chart with pairwise comparison of indicators from a field exercise carried out in the Gangetic Basin.
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Figure A-8, 2: Flow of FGD and subsequent steps (Prabhakar, 2014) The pairwise rankings should be decided after the group reaches a consensus and hence represent the collective opinion of the group. The individual FGD responses are then subjected to aggregation of individual judgment analysis that gives the collective consensus and helps identifying which groups or sections tend to prioritize what. Note: In contrast, Figure 18 shows the two-stage process combining the vulnerability assessment and prioritization of adaptation practices. Users are suggested to design the FGD depending on which approach is followed.
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Photo A-8, 1: Chart showing the pairwise comparisons of indicators in the flood-prone areas of the Gangetic basin
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Table A-8, 3: Saaty’s fundamental scale of judgment Intensity of importance
Definition
Explanation
1
Equal importance
3
Moderate importance
5
Strong importance
7 9
Very strong importance Extreme importance
Two activities contribute equally to the objective Judgment slightly favors one criteria over another Judgment strongly favors one criteria over another A criteria is favored very strongly over another Judgment favoring a criteria is of the highest possible order of affirmation
Source: Saaty, 2008
A8.4 D ATA
ANALYSIS
While there are several tools available for conducting computations using the data obtained from the above FGD process, the methodology suggested here especially focuses on using the software SuperDecisions as it is free to use. The software uses the AHP methodology developed by Prof Saaty (2008) and it provides priority values by normalizing measurements. The software provides facility to arrange goals, criteria, indicators and practices as alternatives in hierarchical manner. In the software, each hierarchy is denoted as cluster and each criteria/indicator/practice as nodes in a cluster. The entire set of clusters and nodes arranged in a hierarchical fashion is denoted as network (Figure A-8, 1 above can be termed as network diagram). Broadly, conducting AHP in the software involves the following steps: 1. Develop a network diagram showing the relation between goal, criteria, indicators and practices (Figure A-8, 3). a. This is nothing but building the exact replica of how the goal, criteria, indicators and practices are evolved in the focused group discussion. This is done by making the cluster and embedding the nodes within each cluster in hierarchical manner. It is important that each cluster has linkages with the cluster before and after it in order to complete the entire hierarchy. 2. Enter the data through comparisons module (Figure A-8, 4) a. After building the network diagram, the data is entered using the comparisons module that can be
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accessed under the Assess/Compare menu or by pressing the shortcut key F5. b. The data should be entered for all combinations among criteria, complete set of comparisons between indicators under all the criteria and complete set of comparisons of practices under all indicators. 3. Obtain weighted super matrix, normal and priorities through different menus in the computation module (Figure A-8, 5). a. These values can be obtained by using the menu items listed under Computation menu of the main interface or by pressing the respective shortcut keys. b. The priority values (PVs), normalized priority values (NPVs) by cluster and idealized priority values (IDV) will help in comparing the performance of the alternatives. c. Computing a table of priority values for all criteria, indicator and practice combinations provides an easy way to compare and understand the trends. The example is given in Table A-8, 4. d. In AHP methodology, the criteria and indicator that has highest normalized priority value suggests the most important criteria and indicator in the order of normalized priority values. 4. Prioritizing the adaptation options: IDVs, NPVs and raw values for practices help determine prioritize the adaptation practices. These values can be obtained by a pressing the keyboard combination of Ctrl+y or going to the menu Computations on the main application window and clicking on Synthesize (refer to Figure A-8, 5. The adaptation practice (termed as alternative) that has idealized priority value as 1 is the most effective adaptation alternative. In comparison, adaptation practice that has 0.2 should be read as having 20% effectiveness of the practice with IDV as 1. When interpreted through NPVs, the practice with highest NPV is the most effective option followed by the ones that have lower NPVs. In the Figure A8, 6, the Pra 2 is the most effective adaptation practice followed by Pra 1 and Pra 3. In the Table A-8, 4, pump for ground water received highest IDV followed by harvesting surface water with negligible difference and hence could be chosen as most effective 134
adaptation options. At this stage, users can impose additional criteria to narrow down the practices in case of a tie as in this case. Additional criteria could include available resources (financial, access to technology and knowledge to install and usage).
Figure A-8, 3: Network diagram established in the SuperDecisions 135
Figure A-8, 4: Entering data for pairwise comparisons
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Figure A-8, 5: Interface for obtaining weighted super-matrix, idealized priorities and normalized priorities
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Figure A-8, 6: Interface showing the idealized and normalized priority values for practices
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Table A-8, 4: Example showing the tabulation of computation data from SuperDecisions Indicators
Availability of water
Criteria Bring effect on policy 0.65025 Replicable 0.62720 Easy to see the 0.65260 benefit Practices Pump for groundwater Availability of water Increase in crop yield Escape drought Cost effectiveness Less investment Priorities normalized by cluster Idealized priorities
Increase in crop yield
Escape drought
0.17192 0.23506 0.20489
0.09684 0.08583 0.06511
Harvesting surface water
Pest control
Cost effectiveness
0.03228 0.02597 0.03319 Alternative crops
Less investment
0.04870 0.02594 0.04421
0.40747 0.36449 0.40262 0.27410 0.19451 0.38522
0.37738 0.42792 0.32979 0.19745 0.39396 0.37729
0.02239 0.09860 0.08745 0.17222 0.20999 0.05561
0.05954 0.03772 0.09113 0.11314 0.01780 0.05826
Drought resistant varieties 0.13322 0.07126 0.08901 0.24308 0.18375 0.12362
1.0000
0.979396
0.144369
0.151234
0.320907
Priorities normalized by cluster 0.77778 0.11111 0.11111
0.64795 0.18260 0.09209 0.03168 0.04568
Source: Prabhakar, 2014
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A8.5 R EFERENCES Batima, P., L. Natsagdorj, N. Batnasan and M. Erdenetuya (2005) Mongolia’s livestock system vulnerability to climate change. In: N. Leary, C. Conde, A. Nyong and J. Pulhin, eds., For Whom the Bell Tolls, Case Studies of Climate Change Vulnerability. London and Sterling: Earthscan. DSS Glossary (2014) DSS Glossary. Available at http://DSSresources.com. Ilori, C. and S.V.R.K. Prabhakar (2014) Adaptation as a problem of decision making: Application of multi-criteria techniques in adaptation decision making. In: Prabhakar, S.V.R.K. (ed.) Adaptation Decision Making Frameworks and Tools: Multi-criteria Decision Making Tools for Prioritizing Adaptation Actions at Community Level, IGES Research Report No 2013-02, Hayama, Japan: Institute for Global Environmental Strategies. Malczewski, J., Moreno-Sánchez, R., Bojórquez-Tapia, L. and Ongay-Delhumeau, E. (1997) Multi-criteria group decision making model for environmental conflict analysis in the Cape Region, Mexico. Journal of Environmental Planning and Management, 40: 349–374. Mu, E. (2005) Using ANP in the non-profit sector: Selecting a congress site and predicting conference attendance. International Symposium on the Analytic Hierarchy Process, Honolulu, Hawaii, July 7-10, 2005. Prabhakar, S.V.R.K. (2014) Adaptation decision making frameworks and tools: Multi-criteria decision making tools for prioritizing adaptation actions at community level. IGES Research Report No.2013–02. Hayama, Japan: Institute for Global Environmental Strategies. Saaty, T.J. (1990) How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 1990, 48: 9-26. Saaty, T.L. (2008) Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1): 83-98.
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Teknomo, K. (2006) Analytic hierarchy process (AHP). Tutorial. Available at http://people.revoledu.com/kardi/tutorial/ahp/. Vreeker, R., P. Nijkamp and C.T. Welle (2002) A multi-criteria decision support methodology for evaluating airport expansion plans, Transportation Research Part D, 7 (1): 27-47. Yin, Y, P. Gong and Y. Ding (2007) Integrated assessments of vulnerabilities and adaptation to climate variability and change in western region of China. In: SCOPE/START (eds.), Changes in Human-Monsoon System of East Asia in the Context of Global Change, Islands Press.
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Contact for More Information: NABARD and ADAPT Asia Pacific Email IDs: sivapuram.prabhakar@gmail.com climate.change@nabard.org
