1. CICERO Center for International Climate and Environmental Research – Oslo Senter for klimaforskning 1. Measuring Vulnerability Karen O’Brien CICERO, University of Oslo Email: karen.obrien@cicero.uio.no START Advanced Institute on Vulnerability May 11, 2004

2. Measuring Vulnerability: 1. Theoretical issues 2. Conceptualizations of vulnerability 3. Practical issues 4. The use of scenarios

3. Definitions of Vulnerability 1. ”an aggregate measure of human welfare that integrates environmental, social, economic and political exposure to a range of harmful perturbations” (Bohle et al. 1994) 2. “…the exposure to contingencies and stress, and difficulty in coping with them. Vulnerability thus has two sides: an external side of risks, shocks and stress to which an individual or household is subject; and an internal side which is defencelessness, meaning a lack of means to cope without damaging loss” (Chambers 1989) 3. ”Vulnerability: the degree to which a system is susceptible to, or unable to cope with, adverse effects of climate change, including climate variability and extremes. (IPCC 2001)

4. Why measure vulnerability? 1. Identify magnitude of threats, such as climate change; 2. Guide decision-making on international aid and investment; 3. Prioritize aid for climate change adaptation; 4. Identify measures to reduce vulnerability.

5. Can vulnerability be measured? Vulnerability is a characteristic, trait, or condition; not readily measured or observable, thus we need proxy measures and indicators; Vulnerability is a characteristic, trait, or condition; not readily measured or observable, thus we need proxy measures and indicators; Vulnerability is relative, not absolute; Vulnerability is relative, not absolute; Everyone is vulnerable, but some are more vulnerable than others; Everyone is vulnerable, but some are more vulnerable than others; Vulnerability relates to consequences or outcomes, and not to the agent itself; Vulnerability relates to consequences or outcomes, and not to the agent itself; Defining levels of vulnerability that prompt actions or interventions is a social and political process. Defining levels of vulnerability that prompt actions or interventions is a social and political process.

6. What is the opposite of vulnerability? Is there an opposite? Is there an opposite? Is it resilience, adaptability, or human security? Is it resilience, adaptability, or human security?

7. Conceptualizing vulnerability Vulnerability can be conceptualized in different ways. Vulnerability can be conceptualized in different ways. Any conceptualization of vulnerability can be interpreted in different ways. Any conceptualization of vulnerability can be interpreted in different ways. Conceptualizations and interpretation of vulnerability have implications for what is measured and how it is measured. Conceptualizations and interpretation of vulnerability have implications for what is measured and how it is measured. Vulnerability measures can have political and economic consequences; transparency (in both concepts and methods) is necessary. Vulnerability measures can have political and economic consequences; transparency (in both concepts and methods) is necessary.

8. Biophysical vulnerability Focuses on ecological processes, exposure to processes of physical change; Focuses on ecological processes, exposure to processes of physical change; Indicators include length of growing season; frost days, intense precipitation, etc. Indicators include length of growing season; frost days, intense precipitation, etc.

9. Social vulnerability Focus on social, political, economic and cultural determinants of vulnerability. Focus on social, political, economic and cultural determinants of vulnerability. Indicators include education, income, and other proxy data (social capital, entitlements, livelihood diversification). Indicators include education, income, and other proxy data (social capital, entitlements, livelihood diversification).

10. Climate change vulnerability IPCC vulnerability framework: V = f(E, S, AC) E = Exposure S = Sensitivity AC = Adaptive Capacity

11. Exposure The degree of climate stress upon a particular unit of analysis The degree of climate stress upon a particular unit of analysis Climate stress: Climate stress: long-term climate conditions long-term climate conditions climate variability climate variability magnitude and frequency of extreme events magnitude and frequency of extreme events

12. Sensitivity The degree to which a system will respond, either positively or negatively, to a change in climate. The degree to which a system will respond, either positively or negatively, to a change in climate.

13. Adaptive Capacity The capacity of a system to adjust in response to actual or expected climate stimuli, their effects, or impacts. The capacity of a system to adjust in response to actual or expected climate stimuli, their effects, or impacts. The degree to which adjustments in practices, processes, or structures can moderate or offset the potential for damage or take advantage of opportunities created by a given change in climate.

14. Interpretation 1: Vulnerability analysis as a means of defining the extent of the climate problem Vulnerability analysis as a means of defining the extent of the climate problem Vulnerability = Impacts – Adaptations Vulnerability = Impacts – Adaptations Adaptability defines vulnerability Adaptability defines vulnerability Vulnerability is the end-point of the analysis Vulnerability is the end-point of the analysis

15. Interpretation 2: Vulnerability analysis as a means of identifying what to do about climate change. Vulnerability analysis as a means of identifying what to do about climate change. Vulnerability is shaped by adaptive capacity. Vulnerability is shaped by adaptive capacity. Vulnerability determines adaptability Vulnerability determines adaptability Vulnerability is the starting point of the analysis. Vulnerability is the starting point of the analysis. Under this interpretation, we need measures of the social processes that contribute to vulnerability. Under this interpretation, we need measures of the social processes that contribute to vulnerability.

16. Implications End point: We need better GCM scenarios, better process models, and better quantifications of adaptation; End point: We need better GCM scenarios, better process models, and better quantifications of adaptation; Starting point: We need better understanding of coping capacity, adaptive capacity, outcomes of social processes, and measures of well-being. Starting point: We need better understanding of coping capacity, adaptive capacity, outcomes of social processes, and measures of well-being.

17. Measuring vulnerability: Practical challenges How should indicators be chosen? How should indicators be chosen? Are adequate data available? Are adequate data available? How should composite indicators be developed? How should composite indicators be developed? How can measures of vulnerability be validated? How can measures of vulnerability be validated?

18. Choosing indicators: Deductive approach Theory driven: Start from theory or hypothesis; find indicators that might support or reject the hypothesis. Theory driven: Start from theory or hypothesis; find indicators that might support or reject the hypothesis. Example: Adger and Kelly (2000) hypothesize that the architecture of entitlements is a key determinant of vulnerability in Vietnam; thus they identify income levels, income inequality and diversity of livelihood as key indicators. Example: Adger and Kelly (2000) hypothesize that the architecture of entitlements is a key determinant of vulnerability in Vietnam; thus they identify income levels, income inequality and diversity of livelihood as key indicators.

19. Choosing indicators: Inductive approach Data driven: Examine lots of data, look for patterns and examine correlations or statistical relationships. Generalizations can be used to develop conceptual models and theories. Data driven: Examine lots of data, look for patterns and examine correlations or statistical relationships. Generalizations can be used to develop conceptual models and theories. Example: Ramachandran and Eastman (1997) analyzed 92 variables to explain the need for food assistance in West Africa. Using statistical methods, they identified the contributions of different variables to vulnerability. Example: Ramachandran and Eastman (1997) analyzed 92 variables to explain the need for food assistance in West Africa. Using statistical methods, they identified the contributions of different variables to vulnerability.

20. Reality: Eriksen and Kelly (submitted) point out that in most national level assessments of vulnerability, the selection of indicators is based on a ”rudimentary theoretical appreciation of vulnerability (which is often, it is only fair to say, all that is available)”. Few ”inductive” indicator studies explicitly discuss implications of findings for vulnerability theory. Eriksen and Kelly (submitted) point out that in most national level assessments of vulnerability, the selection of indicators is based on a ”rudimentary theoretical appreciation of vulnerability (which is often, it is only fair to say, all that is available)”. Few ”inductive” indicator studies explicitly discuss implications of findings for vulnerability theory. Most studies that measure vulnerability are ”not easily distinguishable as either deductive or inductive…” Most studies that measure vulnerability are ”not easily distinguishable as either deductive or inductive…”

21. Data Need for reliable, readily available, and representative data for desired indicators of vulnerability. Need for reliable, readily available, and representative data for desired indicators of vulnerability. Compiling national data is difficult. National level vulnerability assessments often rely on existing global data sets (FAO, World Bank, UNDP, WRI, etc.) Compiling national data is difficult. National level vulnerability assessments often rely on existing global data sets (FAO, World Bank, UNDP, WRI, etc.) More detailed data usually available for sub- national assessments (e.g., census data) More detailed data usually available for sub- national assessments (e.g., census data)

22. Data “Data are usually treated unproblematically except for technical concerns about errors. But data are much more than technical compilations. Every data set represents a myriad of social relations.” “Data are usually treated unproblematically except for technical concerns about errors. But data are much more than technical compilations. Every data set represents a myriad of social relations.” (Taylor and Johnston 1995, p. 58)

23. Social relations exemplified in different sources of irrigation statistics for India Irrigation Department Irrigation Department Irrigation data as basis for repayment of water fee to maintain irrigation facilities Irrigation data as basis for repayment of water fee to maintain irrigation facilities Revenue office Revenue office Irrigation data as basis for land taxes--which are higher for irrigated lands Irrigation data as basis for land taxes--which are higher for irrigated lands Agriculture Department Agriculture Department Supposed to survey all land in the district Supposed to survey all land in the district  No consistency between these sources

24. V.-Dimension Desired variables Empowerment Child sex rate (”missing girls” or excess girl mortality) Female literacy level Literacy level Fertility level Share of landholdings by farm size % Landless agricultural labourers Technology Irrigation rate Infrastructure Development Index (CMIE) Source of irrigation Access to safe drinking water Fertilizer consumption Poverty People below poverty line Infant Mortality Rate Housing status Dependency on agriculture Employment in agriculture

25. V.-Dimension Available data Empowerment Child sex rate (”missing girls” or excess girl mortality) Female literacy level Literacy level % Landless agricultural labourers Technology Irrigation rate Infrastructure Development Index (CMIE) Poverty Dependency on agriculture Employment in agriculture

26. Does the choice of indicators and index matter? ”In one sense, this is an empirical question. The analyst should test different formulations— choices of indicators, transformations, modes of aggregation, variations in data quality, etc. If the overall rankings do not differ much, then one could argue for the simplest formulation. Compiling an index is not however an end in itself. The form of the index may itself be part of the process of getting support for the index and its policy implications.” ”In one sense, this is an empirical question. The analyst should test different formulations— choices of indicators, transformations, modes of aggregation, variations in data quality, etc. If the overall rankings do not differ much, then one could argue for the simplest formulation. Compiling an index is not however an end in itself. The form of the index may itself be part of the process of getting support for the index and its policy implications.” Source: Downing et al. 2001

27. Dynamics of vulnerability Vulnerability is dynamic; indicators are often static. Vulnerability is dynamic; indicators are often static. Snapshots of vulnerability do not tell us who is becoming more vulnerable (or less vulnerable) as time goes on. Snapshots of vulnerability do not tell us who is becoming more vulnerable (or less vulnerable) as time goes on.

28. Creating composite indices Vulnerability is multi-dimensional; there is no one indicator that adequately represents vulnerability. Vulnerability is multi-dimensional; there is no one indicator that adequately represents vulnerability. Composite indices can provide a more complex measure of vulnerability. Composite indices can provide a more complex measure of vulnerability. Many potential methods exist for aggregating indicators (e.g., indiscriminate aggregation, weighted indicators, targeted indicators, contingent indicators, dynamic indicators, heirarchical vulnerability indices, vulnerability profiles) Many potential methods exist for aggregating indicators (e.g., indiscriminate aggregation, weighted indicators, targeted indicators, contingent indicators, dynamic indicators, heirarchical vulnerability indices, vulnerability profiles)

29. Creating composite indices ”Unless a verifiable outcome variable is available, there is no clear reason to choose a particular approach. A guiding principle may be to keep the analysis transparent and accessible to end users.” (Downing et al. 2001) ”Unless a verifiable outcome variable is available, there is no clear reason to choose a particular approach. A guiding principle may be to keep the analysis transparent and accessible to end users.” (Downing et al. 2001)

30. Verifying measures of vulnerability ”Verification conveys authority and credibility, but also contributes to improving the understanding of vulnerability and hence the representation of processes in indicator studies” (Eriksen and Kelly, submitted) ”Verification conveys authority and credibility, but also contributes to improving the understanding of vulnerability and hence the representation of processes in indicator studies” (Eriksen and Kelly, submitted)

31. Verifying measures of vulnerability In the case of deductive approaches, verification involves assessment of goodness of fit between theoretical predictions and empirical evidence. In the case of deductive approaches, verification involves assessment of goodness of fit between theoretical predictions and empirical evidence. In the case of inductive approaches, the statistical analysis must incorporate verification of any results through testing on independent data. In the case of inductive approaches, the statistical analysis must incorporate verification of any results through testing on independent data. Unfortunately, such verification has been limited in existing studies of vulnerability indicators. Unfortunately, such verification has been limited in existing studies of vulnerability indicators. Source: Eriksen and Kelly, article submitted to MASGC

32. Verifying measures of vulnerability Is the outcome acceptable? Is the outcome acceptable? Does the ranking match what people expect based on their experience? Does the ranking match what people expect based on their experience? Can anomalies be explained? Can anomalies be explained? Who should be the judge? Who should be the judge? How can dissenting views be represented? How can dissenting views be represented? Source: Downing et al. 2001

33. Measuring vulnerability: Scenarios When we are concerned about future conditions (e.g., under climate change), and we want to project vulnerability into the future, we need scenarios. When we are concerned about future conditions (e.g., under climate change), and we want to project vulnerability into the future, we need scenarios. Focusing on present-day vulnerability to future climate change can provide a starting point for actions or interventions to reduce vulnerability; less useful for assessing the extent of the climate change problem. Focusing on present-day vulnerability to future climate change can provide a starting point for actions or interventions to reduce vulnerability; less useful for assessing the extent of the climate change problem.

34. Different types of scenarios: Climate change scenarios: Generated by general circulation models (GCMs) or synthetic scenarios (+/- 10% precipitation, 30 cm sea level rise, etc.); Climate change scenarios: Generated by general circulation models (GCMs) or synthetic scenarios (+/- 10% precipitation, 30 cm sea level rise, etc.); The output of GCMs depend on assumptions about greenhouse gas emissions, feedbacks, etc. SRES scenarios represent emissions according to different development trajectories; The output of GCMs depend on assumptions about greenhouse gas emissions, feedbacks, etc. SRES scenarios represent emissions according to different development trajectories; Vulnerability will depend on social and economic trends (economic development, population growth); Vulnerability will depend on social and economic trends (economic development, population growth); However, globalization is creating structural social, economic and political changes, thus extrapolation of trends into the future may not be sufficient to describe the future. However, globalization is creating structural social, economic and political changes, thus extrapolation of trends into the future may not be sufficient to describe the future.

35. Scenarios How can we incorporate future scenarios into measures of vulnerability? How can we incorporate future scenarios into measures of vulnerability? What types of uncertainty are added to vulnerability measures? What types of uncertainty are added to vulnerability measures? How can measures of vulnerability based on scenarios be validated? How can measures of vulnerability based on scenarios be validated?

36. CICERO Center for International Climate and Environmental Research – Oslo Senter for klimaforskning 2. Mapping Vulnerability

37. Why map vulnerability? Vulnerability can be both socially and spatially referenced (it is associated with social and environmental phenomena, which often have locational components); Vulnerability can be both socially and spatially referenced (it is associated with social and environmental phenomena, which often have locational components); Measures of vulnerability can be visualized through mapping, and patterns can be identified and analyzed through spatial analysis (tomorrow’s lecture!). Measures of vulnerability can be visualized through mapping, and patterns can be identified and analyzed through spatial analysis (tomorrow’s lecture!).

38. How to map vulnerability? Mental mapping Mental mapping Remote sensing (NDVI) Remote sensing (NDVI) Geographic Information Systems and Science (GIS) Geographic Information Systems and Science (GIS)

39. Examples of vulnerability maps:

42. The issue of scale National scale assessments of vulnerability (to produce a global map) National scale assessments of vulnerability (to produce a global map) Regional vulnerability assessments (e.g., West Africa) Regional vulnerability assessments (e.g., West Africa) Sub-national vulnerability assessments (e.g., Norway, India) Sub-national vulnerability assessments (e.g., Norway, India)

43. National level vulnerability maps Need indicators common to all countries (comparable time periods, units) Need indicators common to all countries (comparable time periods, units) Present coarse generalizations; hide sub- national variations and ”pockets of vulnerability.” Present coarse generalizations; hide sub- national variations and ”pockets of vulnerability.” Can be useful for broad comparisons, correlation with other national statistics (GHG emissions) Can be useful for broad comparisons, correlation with other national statistics (GHG emissions)

44. Regional-level vulnerability maps Represents differential vulnerability across regions; Represents differential vulnerability across regions; Context-specific indicators can be chosen; Context-specific indicators can be chosen; Potentially greater availability of data (from regional institutions, or compiled from national statistics); Potentially greater availability of data (from regional institutions, or compiled from national statistics); Useful for identification of regional ”hot spots” and policy analysis. Useful for identification of regional ”hot spots” and policy analysis.

45. Sub-national vulnerability maps Represents variations in vulnerability within one country, state, county, district, or village; Represents variations in vulnerability within one country, state, county, district, or village; Potentially larger amount of data available (but large data gaps can still exist); Potentially larger amount of data available (but large data gaps can still exist); Can be used to develop national adaptation strategies, aid distribution, development plans, etc. Can be used to develop national adaptation strategies, aid distribution, development plans, etc.

46. Challenges Integrating raster and vector or biophysical and social data; Integrating raster and vector or biophysical and social data; Normalization and weighting of indicators; Normalization and weighting of indicators; Classification Classification

47. Example of Mapping Approach Vulnerability of Agriculture to Climate Change in Norway Vulnerability of Agriculture to Climate Change in Norway

53. Indicators of biophysical vulnerability: Agricultural sector Spring rainfall Spring rainfall Autumn rainfall Autumn rainfall Length of growing season Length of growing season Spring frost/thaw Spring frost/thaw Autumn frost/thaw Autumn frost/thaw Snow depth Snow depth

55. Indicators of social vulnerability: Climate sensitivity Employment in agricultural sector, % Employment in agricultural sector, % Economic capacity Untied public income (taxes and govt. transfers), NOK Untied public income (taxes and govt. transfers), NOK Employment growth prognosis, % Employment growth prognosis, % Demographic capacity Dependency rate, % Dependency rate, % Aging working population, % Aging working population, % Net migration rate, avg. 91-01 % Net migration rate, avg. 91-01 %

57. How correct are these indicators? Case studies must be carried out to verify the indicators selected, and identify factors that shape vulnerability in Norwegian municipalities. Case studies must be carried out to verify the indicators selected, and identify factors that shape vulnerability in Norwegian municipalities. Stakeholder dialogues: Voss and Oppdal Stakeholder dialogues: Voss and Oppdal

58. Mapping Vulnerability to Multiple Stressors: Climate Change and Globalization in India Karen O’Brien 1, Robin Leichenko 2, Ulka Kelkar 3, Henry Venema 4, Guro Aandahl 1, Heather Tompkins 1, Akram Javed 3, Suruchi Bhadwal 3, Stephan Barg 4, Lynn Nygaard 1, Jennifer West 1 1 CICERO 2 Rutgers University 3 TERI 4 IISD

59. Indian agriculture Agriculture is the dominant economic sector (employs 68% of the population) Agriculture is the dominant economic sector (employs 68% of the population) Highly vulnerable to climate variability and climate change Highly vulnerable to climate variability and climate change Undergoing rapid economic changes, presently threatened by globalization (especially import competition, removal of domestic subsidies) Undergoing rapid economic changes, presently threatened by globalization (especially import competition, removal of domestic subsidies) Appropriate example for investigation of vulnerability to multiple stressors Appropriate example for investigation of vulnerability to multiple stressors

60. Mapping Vulnerability to Multiple Stressors 1) develop a regional vulnerability profile for climate change 2) develop a regional vulnerability profile for an additional stressor (in this case globalization) 3) superimpose the profiles to identify districts that are “double exposed;” and 4) investigate double exposure at the local level via case studies

61. Step 1: Develop Profile of Vulnerability to Climate Change Operationalized the IPCC-based definition of Vulnerability (McCarthy et al. 2001) Operationalized the IPCC-based definition of Vulnerability (McCarthy et al. 2001) Vulnerability to climate change is a function of adaptive capacity, sensitivity, and exposure Vulnerability to climate change is a function of adaptive capacity, sensitivity, and exposure

62. Defining Adaptive Capacity, Sensitivity and Exposure Adaptive capacity: the ability of a system to adjust to actual or expected climate stresses, or to cope with the consequences (a function of current social- economic-technological conditions) Adaptive capacity: the ability of a system to adjust to actual or expected climate stresses, or to cope with the consequences (a function of current social- economic-technological conditions) Sensitivity: the degree to which a system will respond to a change in climate, either positively or negatively (we based this current climatic conditions) Sensitivity: the degree to which a system will respond to a change in climate, either positively or negatively (we based this current climatic conditions) Exposure relates to the degree of future climate stress upon a particular unit of analysis (we based this on projected climatic change) Exposure relates to the degree of future climate stress upon a particular unit of analysis (we based this on projected climatic change)

63. Operationalizing Adaptive Capacity A function of a combination of social, economic and technological factors A function of a combination of social, economic and technological factors Social: literacy, gender equality Social: literacy, gender equality Economic: agriculture share of labor force, land ownership Economic: agriculture share of labor force, land ownership Technological:quality of infrastructure and availability of irrigation Technological:quality of infrastructure and availability of irrigation Additive index, normalized and scaled: higher adaptive capacity implies lower vulnerability Additive index, normalized and scaled: higher adaptive capacity implies lower vulnerability

64. Adaptive Capacity

65. Operationalizing Sensitivity and Exposure Sensitivity: function of dryness and monsoon dependence under normal climate Sensitivity: function of dryness and monsoon dependence under normal climate Exposure: Alter the sensitivity index using climate change scenarios (downscaled HadRM2 model) Exposure: Alter the sensitivity index using climate change scenarios (downscaled HadRM2 model) Additive index, normalized and scaled so that highest sensitivity under exposure implies highest vulnerability Additive index, normalized and scaled so that highest sensitivity under exposure implies highest vulnerability

66. Sensitivity and Sensitivity Under Exposure

67. Climate Change Vulnerability Summed adaptive capacity with sensitivity under exposure Summed adaptive capacity with sensitivity under exposure Reveals current vulnerability to future climate change Reveals current vulnerability to future climate change

68. Climate Change Vulnerability Climate Change Vulnerability

69. Step 2: Develop Profile of Vulnerability to Globalization Agricultural trade liberalization a key dimension of globalization for Indian agriculture Agricultural trade liberalization a key dimension of globalization for Indian agriculture Focus on import competition Focus on import competition Used IPCC typology of adaptive capacity, sensitivity and exposure Used IPCC typology of adaptive capacity, sensitivity and exposure

70. Operationalizing Globalization Vulnerability Adaptive capacity: same definition as used for climate change adaptive capacity Adaptive capacity: same definition as used for climate change adaptive capacity Sensitivity (and exposure) to import competition: crop productivity, production patterns and distance to ports Sensitivity (and exposure) to import competition: crop productivity, production patterns and distance to ports low productivity, high shares of production in import competing crops, and close proximity to ports make an area more sensitive to competition from international imports low productivity, high shares of production in import competing crops, and close proximity to ports make an area more sensitive to competition from international imports

71. Globalization vulnerability

72. Step 3: Identify areas of double exposure Overlay climate change and globalization vulnerability profiles to identify areas that are double-exposed Overlay climate change and globalization vulnerability profiles to identify areas that are double-exposed Use the information to inform policy and to suggest areas for case study research Use the information to inform policy and to suggest areas for case study research

73. Double Exposure

74. Summary Our approach reveals relative distribution of vulnerability to multiple stressors Our approach reveals relative distribution of vulnerability to multiple stressors Areas of double exposure need special attention from policy makers Areas of double exposure need special attention from policy makers Vulnerability concept applies to a wide range of stressors -- human dimensions work informs other social science research Vulnerability concept applies to a wide range of stressors -- human dimensions work informs other social science research Need to combine macro profiles with local- level investigation Need to combine macro profiles with local- level investigation

75. Key findings relevant to vulnerability mapping Need to ”ground truth” the maps; Need to ”ground truth” the maps; Not all factors contributing to vulnerability can be captured in quantitative indicators (e.g., institutional factors, policies); Not all factors contributing to vulnerability can be captured in quantitative indicators (e.g., institutional factors, policies); Vulnerability changes over time. Vulnerability changes over time.

76. Social Vulnerability in India

77. Two components of vulnerability

78. How useful are vulnerability maps? Developing vulnerability measures and maps moves ”vulnerability science” forward; they force us to clarify concepts; address methodological challenges; interrogate assumptions, hypotheses, and the processes that contribute to vulnerability; Developing vulnerability measures and maps moves ”vulnerability science” forward; they force us to clarify concepts; address methodological challenges; interrogate assumptions, hypotheses, and the processes that contribute to vulnerability; They provide a means of depicting differential vulnerability; They provide a means of depicting differential vulnerability; The output maps can be dangerous if the concepts and methods are not transparent, and if they are taken as reality, rather than as one representation of reality. The output maps can be dangerous if the concepts and methods are not transparent, and if they are taken as reality, rather than as one representation of reality.

79. ”All maps state an argument about the world” (Brian Harley) ”All maps state an argument about the world” (Brian Harley) Know your concepts Know your concepts Know your data Know your data Know your case Know your case

80. CICERO Center for International Climate and Environmental Research – Oslo Senter for klimaforskning Hands On Exercise: Mapping Vulnerability in India START Advanced Institute on Vulnerability May 11, 2004

81. When mapping vulnerability, how do we define and assign different levels of vulnerability?

82. Issue that we will address: Normalization Normalization Weighting Weighting Classification Classification

83. Normalization HDI method (UNDP): Normalization to the range HDI method (UNDP): Normalization to the range But to which range? But to which range?

84. Fixing of ”goalposts” for max and min values Comparison in space Comparison in space Who should we measure against? Who should we measure against? Comparison in time Comparison in time Retrospective: What has happened in earlier periods? Retrospective: What has happened in earlier periods? Prospective: What are projections for the future? Prospective: What are projections for the future? (reference: Anand and Sen 1994)

85. Goalposts: two alternatives 1. Use the actually occurring range or 2. Use predefined maximum and minimum values

86. Goalposts: actual range or predefined? Indicator Occuring range [1991,2001] Independent min and max Agricultural dependency 6,6% - 94,7% 0% - 100% Agricultural labourers 0,06% - 88,25% 0% – 100% Literacy 13,7% - 95,7% 10%– 100% Female literacy 4,2% - 93,97% 0% – 100% ”Missing girls” 43,2% - 48,5%* 40,0% - 48,5%*

87. Normalization: range (2) vs predefined max and min (3)

88. Normalization: range (2) vs predefined max and min (3) - impact on ranks

89. Weighting We gave equal weighting to the three components of adaptive capacity; and an equal weighting between adaptive capacity and sensitivity/exposure. Other weightings were tested, but without a priori reasons for weighting one index higher than another, it was considered best to keep it simple. We gave equal weighting to the three components of adaptive capacity; and an equal weighting between adaptive capacity and sensitivity/exposure. Other weightings were tested, but without a priori reasons for weighting one index higher than another, it was considered best to keep it simple.

90. Classification Can exaggerate non-significant differences Can exaggerate non-significant differences Can hide significant differences Can hide significant differences

91. Data distribution for social index, 1991

92. Data distribution for social index, 1991 – natural breaks (minimized variance within groups)

93. Data distribution for social index, 1991 – quantiles (groups are equal size, 20% of pop)

95. Classification: natural breaks (nb) vs quantiles (qnt)

96. Exercise We will map climate change vulnerability in India, using different weightings and classifications. The first objective of the exercise is to explore how sensitive or robust vulnerability maps are to such decisions. The second objective is to change the composition of the index and try to create a map that depicts the eastern coast of India (including Orissa) as highly vulnerable. We will map climate change vulnerability in India, using different weightings and classifications. The first objective of the exercise is to explore how sensitive or robust vulnerability maps are to such decisions. The second objective is to change the composition of the index and try to create a map that depicts the eastern coast of India (including Orissa) as highly vulnerable.

97. Files: Excel spreadsheet with district-level indicators for India; Excel spreadsheet with district-level indicators for India; Shape files for district and state boundaries; Shape files for district and state boundaries;

98. The following questions should be answered: How sensitive is the map to weighting and classification methods? How sensitive is the map to weighting and classification methods? How easily can indicators be manipulated to show whatever you want to show? How easily can indicators be manipulated to show whatever you want to show? What are the policy or political consequences of these findings? What are the policy or political consequences of these findings? How can we prevent misuse of vulnerability maps? How can we prevent misuse of vulnerability maps?

99. GIS Introduction to ArcGIS Introduction to ArcGIS