Supplementary Information for the paper:

SCENARIOS FOR VULNERABILITY

Opportunities and constraints in the context of climate change and disaster risk

Authors: Joern Birkmann*a, Susan Cutterf, Dale S. Rothmanb, Torsten Wellea, Matthias Garschagena, Bas van Ruijvenc, Brian O’Neillc, Benjamin Prestond, Stefan Kienbergere, Omar. D. Cardonah, Tiodora Siagiani, Deny Hidayatij, Neysa Setiadia, Claudia Binderk, Barry Hughesb and Roger Pulwartyl

aUnited Nations University, Institute for Environment and Human SecuritybUniversity of Denver, Pardee Center for International FuturescNational Center for Atmospheric Research (NCAR)dClimate Change Science Institute, Oak Ridge National Laboratory eUniversity of Salzburg, Centre for GeoinformaticsfUniversity of South Carolina, Hazards and Vulnerability Research InstitutehNational University of Colombia, Manizales (UNC)iGovernment of Indonesia, Statistics Indonesia (BPS)jIndonesian Institute of Sciences (LIPI)kUniversity of Munich (LMU), Department for GeographylNational Oceanic & Atmospheric Administration, Earth System Research Laboratory

The following supplement provides additional information regarding:

S1 Components and indicators of the WorldRiskIndex

S2 Calculation of exposure of the WorldRiskIndex (including hazard frequency)

S3 Calculation of susceptibility of the WorldRiskIndex

S4 Validity and Robustness of the WorldRiskIndex

S5 Exposure related to World Bank income groups

S6 Susceptibility on global level and per World Bank income groups

S7 Description of the International Futures system (IFs model)

S8 IFs model: Descriptions of the three scenarios

S9 Case study Jakarta: Scenario methodology and background data

S1 Components and indicators of the WorldRiskIndex

Indicator selection

The process of indicator selection was done through a questionnaire, where the relevance of suggested indicators was distributed to several experts and practitioners of aid and development organisations in Germany (e.g. Bread for the World; Misereor, Welthungerhilfe) with different backgrounds and working experience in various countries. In addition to the judgement regarding the importance of single indicators, respondents had the opportunity to suggest new potential indicators or relevant criteria that were missing from the provided list. This feedback ensured the relevance of proposed indicators not only from a theoretical but also from a praxis-oriented viewpoint. Besides this selection criteria the chosen indicators fulfilled the standard criteria for indicator development such as measurability, representing an issue that is important to the relevant topic, policy-relevance, only measuring important key-elements instead of trying to indicate all aspects, analytically and statistically sound, understandable, easy to interpret, sensitivity and specificity to the underlying phenomenon, validity/accuracy, reproducibility, based on available data, data comparability, appropriate scope and cost effectiveness (see also EEA, 2004; Birkmann, 2006; Gallopín, 1997).

Components: exposure, susceptibility, coping and adaptation

a.) The exposure component in this index encompasses the natural hazard sphere and aims to identify entities exposed and prone to being affected by a hazard event. These entities include communities, resources, infrastructure, production, goods, services and ecosystems (UNISDR, 2009). Within the WorldRiskIndex, exposure is related to physical exposure, which means the potential average annual number of individuals who are exposed to earthquakes, storms, droughts and floods (Peduzzi et al., 2009). These data were obtained from the PREVIEW Global Risk Data Platform. Sea-level rise exposure was derived based on population statistics of the Global Rural-Urban Mapping Project (GRUMP) run by the Center for International Earth Science Information Network (CIESIN) at Columbia University, and sea-level data by one metre from the Center for Remote Sensing of Ice Sheets (CReSIS) at the University of Kansas using a zonal statistics approach within the ArcGIS 10 program.

b.) Susceptibility refers to the conditions of exposed communities or other exposed elements that make them more likely to experience harm and to be negatively affected by a natural hazard or by climate change. Thereby susceptibility is closely linked to structural characteristics such as nutrition, economic capacities and access as well as quality of public infrastructure that allow a first evidence of relative susceptibilities of societies. Thus susceptibility can be understood as the likelihood and propensity to suffer harm and damage in case of the occurrence of a natural hazard.

c.) Coping capacities are societal response capacities to natural hazards which are defined as the ability of a society or group, organisation or system to use its own resources to face and manage emergencies, disasters or adverse conditions that

could lead to a harmful process caused by a hazard event (UNISDR, 2009). Coping, in this study, is – compared to adaptation – a direct response to the impact of a given hazard event.

d.) Adaptation is defined as a long-term strategy that might be linked to a certain hazard. Thus adaptation encompasses capacities, measures and strategies that enable communities to change and to transform in order to deal with expected negative consequences of natural hazards and climate change.

S1.1 Indicators for susceptibility

Population without access to improved sanitation facilities Relevance: A population without access to improved sanitation facilities is an indicator of the quality of and access to basic infrastructure, demonstrating quality-of-life and basic health condition of the population.

Population without access to an improved water sourceRelevance: People without improved water sources are vulnerable to diseases caused by unclean water and could become more vulnerable in the aftermath of a hazard, due to their existing ailments. However, improved water sources (based on the assumption they are likely to provide safer water) can significantly lower the risk of water-borne diseases, which in turn has a positive impact on people’s health status.

Percentage of population undernourishedRelevance: The indicator illustrates the problems of food insecurity and hunger of a population, which has serious consequences on people’s physical condition and health and very negative impacts on the mental and physical development of children.

Dependency ratioRelevance: As the ratio of the economically dependent population to the income generating population, a high value increases the susceptibility to harm as more people are affected (economically dependent population) if a working person experiences harm.

Extreme poverty (poverty headcount ratio living on $1.25 USD per day (PPP))Relevance: Poverty is the deprivation of essential goods, services and opportunities. Poor people are more susceptible to suffer from the impact of natural hazards, as they tend to live in hazard-prone areas (e.g. in unsafe buildings, on floodplains, etc.) and continually have to cope with various shocks related to hazards, in dire conditions with limited assets.

Gross domestic product (GDP) at purchasing power parity (PPP) per capita (current international in USD)Relevance: GDP per capita in PPP has been identified as an important determinant of susceptibility and vulnerability by different authors and used in the Disaster Risk

Index 2004 (Peduzzi et al., 2009; Schneiderbauer, 2007; UNDP, 2004) and is commonly used as an indicator for a country’s economic development.

Gini IndexRelevance: The index gives an estimate of inequality as it measures the extent to which the actual income distribution differs from an equal distribution. The higher the index the higher the inequality. It is widely recognised in vulnerability research that a high level of inequality can be seen as a characteristic of susceptibility and vulnerability.

S2 Calculation of exposure of the WorldRiskIndex (including hazard frequency)

The calculation of physical exposure for floods, cyclone and droughts is based on data from the Global Risk Data Platform PREVIEW (Peduzzi et al., 2009; Birkmann et al., 2011; Welle et al., 2012). For the assessment of physical exposure to sea-level rise, data of the University of Kansas, Center for Remote Sensing of Ice Sheets (CreSIS) was used and combined with GRUMP (Global Rural-Urban Mapping Project) population data of Columbia University, Center for International Earth Science Information Network (CIESIN). Based on these datasets we calculated the exposure using different weights for different hazards (Welle et al., 2012). After calculating the exposure for 2010 for every country (including a scenario for sea-level rise), we assumed that the rate of population growth in hazard exposed versus non-hazard exposed areas in each country is the same between 2010 and 2035. This assumption was necessary, since global scenarios of population growth do not differentiate between the populations exposed to a certain hazard versus the population not exposed. Consequently, the calculation of exposure patterns for different hazards had to be based on a second data set (PREVIEW and GRUMP) as shown above (see also 1. Components and indicators of the WorldRiskIndex – exposure).

S3 Calculation of susceptibility of the WorldRiskIndex

In terms of the calculation of susceptibility, the IFs model provides data for: the population without access to improved sanitation (x1), the population without access to clean water (x2), the population undernourished (x3), the share of population under 15 and over 65 years old in relation to the working population (x4), the population living with $1.25 USD per day - or less - (x5), the gross domestic product (GDP) per capita (purchasing power parity) (x6) and the Gini Index (x7) (overview Table 1 in the paper) for the years 2010 to 2035 based on respective modelling results and with a national scale resolution (Hughes et al., 2011). IFs is currently unable to provide forecasts of numbers of physicians, numbers of hospital beds, insurances, share of female representation, water resources, biodiversity and habitat protection, and forest and agricultural management. Hence core aspects of coping and adaptation cannot yet been visualised and modelled in the scenarios of the Integrated Assessment Modelling (IAM) community.

The modelling of these individual indicators is based on a large number of parameters and sub-models, as well as assumptions of the interrelations between different factors (Hughes et al., 2011, pp. 30-32 and also supplemental material to this paper). For vulnerability and risk research, the question is whether these scenarios can provide useful information at a global scale. In order to explore this question, we compare the baseline data for the current situation of susceptibility in different countries using data for the year 2010 from IFs. All indicators were transformed into dimensionless units between 0 and 100.

Susceptibility is calculated using the following formula; a country with maximum susceptibility would have a susceptibility value of 100:

Susceptibility = 2/7*(1/2*(x1 + x2)) + 1/7*x3 + 2/7*(1/2*(x4 + x5) + 2/7*(1/2*(x6 + x7))

S4 Validity and Robustness of the WorldRiskIndex

A reliability analysis was undertaken for the entire WorldRiskIndex and the respective indicators. That means a correlation test between the model output for the vulnerability index (which is the sum of susceptibility, coping and adaptive capacity) and all input variables (23 indicators, see indicators in table 1) was conducted. The criteria for a good inherent consistence of the model are given in values greater than 0.9 for Cronbach’s Alpha and Guttman’s Lambda. The result for the Cronbach’s Alpha is 0.941 and Guttman’s Lambda is 0.961. Both results underscore that there is a good fit and consistency between the model output and the vulnerability indicators selected.

In a second step a factor analysis was undertaken in order to validate the aggregation formula of the WorldRiskIndex (Birkmann et al., 2011, Welle et al., 2012). A perfect aggregation would result in a Kaiser-Meyer-Olkin Measure Accuracy of 1, the result of the factor analysis of the used aggregation formula was 0.769, and thus the results can be defined as reliable. Since exposure is a quite different factor compared to the other socio-economic characteristics that describe the vulnerability of societies to climatic hazards, the accuracy test of Kaiser-Meyer-Olkin (KMO) was conducted also for the vulnerability component alone. The result for this analysis of the accuracy of the vulnerability components showed a KMO value of 0.938, which means that the unexplained variance in the vulnerability component is very small. KMO-values between 0.8 and 0.9 are ranked as good fits, and values above 0.9 as excellent fits.

Further consequences of the factor analyses are the factor loadings which represent the correlation between each component and the overall index value. The factor loading for susceptibility is -0.936, for coping 0.945 and for adaptation 0.960. The fact that these three components have more or less the same factor loadings means that each component has a strong correlation with the overall index and therefore it makes sense to view these components as individual components and to weight them equally.

Within a third step a sensitivity analysis was conducted in order to test the confidence in the model and its predictions, by providing an understanding on how the model variables and the output respond to changes in the inputs (Saltelli, 2000). The result of the sensitivity analysis for the vulnerability index is displayed in Figure S.1. The exposure component was not included since this component cannot be influenced by society or stakeholders easily and has, per se, the strongest influence with respect to the model output. The left part of Figure S.1 shows each indicator displayed as a curve. The x-axis shows the original input data of each indicator scaled between -0.5 and +0.5 and the y-axis displays the variance of these indicators (scaled between 0 and 1). Every curve (indicator) shows the strength of influence of each indicator on the index. The stronger the influences of the indicator the steeper the curves will look like. Hence indicators which have nearly now influence would appear as a horizontal line. Consequently, Figure S.1 and results show that every indicator has a significant influence on the overall index result.

Figure S.1. Result of the sensitivity analysis for the vulnerability index (own figure)

The middle part of Figure S.1 shows box plots with the different indicators on the x-axis and the sensitivity on the y-axis. The size of the boxes explains how precise the indicators influence the index. The smaller the box the more precise is the influence on the index. The bold line in each box describes the median, whereas high values on the y-axis explain the strength of the influence of each indicator to the overall index. The right part of Figure S.1 also shows box plots that display the influence and interaction of each indicator with the others in case of omission of one indicator.

In a fourth step the robustness of the vulnerability ranks were tested compared to the methodological approach. Therefore all 23 societal indicators were used (see table 1) to check the ranks of the vulnerability index not taking into account the exposure. A Monte Carlo simulation with a total of 300 simulations was run in order to cover the space of uncertainties present in the vulnerability index. The results for the 173

countries are given in Table S.1, where 131 countries lie within the confidence interval for the median rank and additionally this confidence interval is narrow enough (less than 20 positions) to allow for reliable inference on those ranks (Saisana and Saltelli, 2010). However 42 country ranks have to be treated with caution: some of them lie in the confidence interval but the confidence interval is more than 20 positions such as for Norway, Finland, Luxembourg, Barbados and United Kingdom. Also some countries lay just under the first value of the confidence interval e.g. Central African Republic, Guinea, Liberia and Haiti but would fit within the direction of the confidence interval and the calculated median. Overall 15 countries did not lie within the confidence intervals and only Uganda (rank: 149) shows the highest difference between vulnerability rank and confidence interval. However, these ranking results confirm that the overall results are quite reliable, since only about 9 per cent of the cases do not lie within the confidence interval and hence have to be treated with caution. Overall, the vulnerability index calculated within the methodology of the WorldRiskIndex can be seen as reliable methodology approach resulting in reasonable and robust results.

Table S.1. Vulnerability ranks with uncertainty consideration; vulnerability rank of 69 countries is very robust to the methodology approach and the ranks are highly reliable, vulnerability rank of 62 countries is sensitive to the methodology approach within acceptable limits so the ranks are reliable and vulnerability rank of 42 countries is very sensitive to the methodology approach and the rank has to be treated with caution.

NAME Low vulnera

bility rank

Median [99 per cent confidence

interval]

Validation results

Norway 1 11 [1,28] rank treated with cautionIceland 2 2 [2,3] rank highly reliableSweden 3 3 [2,3] rank highly reliableSwitzerland 4 4 [3,4] rank highly reliableFinland 5 16 [3,33] rank treated with cautionAustria 6 6 [5,6] rank highly reliableNetherlands 7 7 [7,8] rank highly reliableDenmark 8 8 [8,13] rank highly reliableGermany 9 15 [10,27] rank reliableNew Zealand 10 9 [9,14] rank highly reliableLuxembourg 11 26 [11,50] rank treated with cautionJapan 12 12 [12,19] rank highly reliableBelgium 13 13 [13,20] rank highly reliableFrance 14 14 [14,21] rank highly reliableAustralia 15 15 [15,22] rank highly reliableIreland 16 16 [1,16] rank reliableCanada 17 20 [17,29] rank reliableUnited Kingdom 18 27 [18,41] rank treated with cautionSingapore 19 26 [19,36] rank reliableUnited States 20 14 [6,20] rank reliableKorea, Republic of 21 16 [8,21] rank reliableSlovenia 22 22 [9,32] rank reliableBarbados 23 34 [23,75] rank treated with cautionSpain 24 29 [24,39] rank reliableCzech Republic 25 21 [12,25] rank reliableEstonia 26 26 [5,29] rank reliableItaly 27 35 [27,45] rank reliableGreece 28 26 [17,29] rank reliableUnited Arab Emirates 29 29 [27,35] rank reliablePortugal 30 28 [19,30] rank reliablePoland 31 31 [26,39] rank reliableSlovakia 32 40 [32,79] rank treated with cautionQatar 33 40 [33,54] rank treated with cautionLithuania 34 34 [13,37] rank treated with cautionMalta 35 66 [45,90] rank treated with cautionUruguay 36 43 [35,57] rank treated with cautionCuba 37 36 [11,36] rank treated with cautionHungary 38 47 [37,64] rank treated with caution

Cyprus 39 50 [39,66] rank treated with cautionCroatia 40 37 [9,38] rank treated with cautionIsrael 41 51 [40,68] rank treated with cautionLatvia 42 38 [14,41] rank treated with cautionBrunei Darussalam 43 37 [13,42] rank treated with cautionBahamas 44 43 [40,43] rank reliableBulgaria 45 66 [44,90] rank treated with cautionBelarus 46 45 [44,70] rank treated with cautionChile 47 36 [19,47] rank treated with cautionArgentina 48 37 [21,48] rank treated with cautionCosta Rica 49 40 [25,50] rank treated with cautionRussia 50 48 [28,50] rank treated with cautionKuwait 51 59 [51,71] rank reliableMauritius 52 43 [29,52] rank treated with cautionBahrain 53 57 [53,65] rank reliableKazakhstan 54 54 [36,55] rank reliableOman 55 53 [44,55] rank reliableSerbia 56 53 [44,56] rank reliableUkraine 57 57 [38,57] rank reliableRomania 58 50 [38,58] rank reliableSeychelles 59 57 [48,59] rank reliableThe former Yugoslav Republic of Macedonia

60 64 [60,72] rank reliable

Trinidad and Tobago 61 52 [40,61] rank treated with cautionSaudi Arabia 62 62 [43,62] rank reliableMalaysia 63 70 [63,82] rank reliableBrazil 64 58 [47,64] rank reliableLebanon 65 75 [65,88] rank treated with cautionGeorgia 66 66 [54,86] rank treated with cautionMexico 67 62 [50,68] rank reliableAzerbaijan 68 70 [69,74] rank reliableTurkey 69 80 [69,93] rank treated with cautionJordan 70 71 [71,76] rank reliableVenezuela 71 66 [53,73] rank reliableGrenada 72 66 [54,73] rank reliablePanama 73 67 [53,74] rank treated with cautionAlbania 74 69 [57,75] rank reliableThailand 75 71 [60,76] rank reliableRepublic of Moldova 76 78 [77,79] rank highly reliableJamaica 77 72 [61,78] rank reliableBosnia and Herzegovina 78 74 [64,79] rank reliableTunisia 79 75 [65,80] rank reliableSuriname 80 76 [66,81] rank reliableArmenia 81 82 [73,82] rank reliableLibyan Arab Jamahiriya 82 86 [83,91] rank reliableSouth Africa 83 81 [75,85] rank reliableChina 84 81 [76,85] rank reliable

Iran (Islamic Republic of) 85 98 [86,111] rank treated with cautionEcuador 86 84 [78,87] rank reliableEgypt 87 92 [88,96] rank reliableFiji 88 85 [80,90] rank reliableBotswana 89 91 [88,94] rank highly reliableSamoa 90 93 [91,97] rank reliableTurkmenistan 91 96 [91,100] rank reliableMongolia 92 88 [82,93] rank reliableColombia 93 89 [82,94] rank reliableBelize 94 90 [84,95] rank reliablePeru 95 91 [85,96] rank reliableDominican Republic 96 92 [87,97] rank reliableViet Nam 97 96 [91,98] rank reliableKyrgyzstan 98 100 [99,101] rank reliableGuyana 99 101 [99,103] rank highly reliableAlgeria 100 98 [95,101] rank highly reliableTonga 101 113 [104,122] rank treated with cautionEl Salvador 102 101 [98,102] rank highly reliableGabon 103 108 [103,113] rank reliableSri Lanka 104 103 [100,105] rank highly reliablePhilippines 105 105 [102,106] rank highly reliableCape Verde 106 109 [106,112] rank reliableSyrian Arab Republic 107 106 [104,109] rank highly reliableUzbekistan 108 115 [108,121] rank reliableEquatorial Guinea 109 108 [105,110] rank highly reliableMorocco 110 109 [106,111] rank highly reliableParaguay 111 110 [107,112] rank highly reliableNamibia 112 111 [108,113] rank highly reliableHonduras 113 112 [109,115] rank highly reliableBhutan 114 117 [114,119] rank highly reliableIndonesia 115 113 [110,116] rank highly reliableNicaragua 116 116 [114,117] rank highly reliableTajikistan 117 118 [115,126] rank reliableVanuatu 118 117 [115,119] rank highly reliableBolivia 119 118 [116,120] rank highly reliableGuatemala 120 119 [117,121] rank highly reliableKiribati 121 127 [123,130] rank treated with cautionSao Tome and Principe 122 121 [119,122] rank highly reliableLao People's Democratic Republic

123 124 [122,124] rank highly reliable

Solomon Islands 124 130 [125,131] rank reliableIndia 125 125 [124,127] rank highly reliableDjibouti 126 130 [126,133] rank reliableGhana 127 127 [124,129] rank highly reliableIraq 128 132 [128,136] rank reliableGambia 129 134 [131,138] rank treated with cautionSwaziland 130 129 [127,130] rank highly reliable

Myanmar 131 129 [123,132] rank reliableCambodia 132 131 [129,134] rank highly reliableNepal 133 138 [135,141] rank treated with cautionSenegal 134 135 [132,136] rank highly reliableCameroon 135 136 [134,138] rank highly reliableLesotho 136 142 [138,144] rank treated with cautionCongo 137 138 [136,139] rank highly reliablePapua New Guinea 138 143 [139,146] rank reliableRwanda 139 139 [135,141] rank highly reliableBangladesh 140 141 [139,143] rank highly reliablePakistan 141 141 [140,143] rank highly reliableAngola 142 143 [142,144] rank highly reliableKenya 143 145 [143,145] rank highly reliableZambia 144 146 [146,154] rank treated with cautionCote d'Ivoire 145 147 [146,148] rank reliableZimbabwe 146 149 [145,155] rank reliableYemen 147 153 [147,160] rank reliableMalawi 148 149 [147,150] rank highly reliableUganda 149 130 [127,133] rank treated with cautionSudan 150 149 [148,151] rank highly reliableTimor-Leste 151 151 [149,153] rank highly reliableBenin 152 151 [149,153] rank highly reliableUnited Republic of Tanzania

153 153 [151,155] rank highly reliable

Mauritania 154 154 [152,155] rank highly reliableGuinea-Bissau 155 155 [154,157] rank highly reliableComoros 156 158 [157,159] rank reliableBurkina Faso 157 156 [156,157] rank highly reliableMadagascar 158 159 [157,161] rank highly reliableTogo 159 160 [157,161] rank highly reliableNigeria 160 161 [159,162] rank highly reliableBurundi 161 162 [161,166] rank highly reliableMali 162 163 [162,163] rank highly reliableEthiopia 163 164 [163,164] rank highly reliableCentral African Republic 164 166 [166,166] rank treated with cautionGuinea 165 167 [167,168] rank treated with cautionMozambique 166 168 [166,168] rank highly reliableLiberia 167 169 [169,170] rank treated with cautionSierra Leone 168 170 [169,170] rank reliableHaiti 169 171 [171,171] rank treated with cautionAfghanistan 170 172 [170,173] rank highly reliableChad 171 173 [171,173] rank highly reliableNiger 172 165 [164,165] rank treated with cautionEritrea 173 173 [173,173] rank highly reliable

S5 Exposure related to World Bank income groups

The results of the exposure assessment show (Figure S.2) that globally most people would be exposed based on population estimation of the “Security First” scenario, followed by the latest base scenario, and fewer people would be exposed in a sustainable world assumption. Using the World Bank analytical classification presented in the World Development Indicators, which is based on the gross net income per capita (GNI) converted to US dollars using the World Bank Atlas method (World Bank, 2013) a different picture of exposed people is visible. In particular, the high-income countries show in all scenarios the same slope of the curve until year 2017, thereafter the slopes of the curves decrease until the year 2025, followed by a decrease in number of exposed people. Compared to low-income countries no decrease in exposed people is shown for all scenarios.

Figure S.2. Estimated physical exposure in millions worldwide (top) and World Bank income groups based on three different scenarios.

S6 Susceptibility on global level and per World Bank income groups

While the high income countries have the lowest susceptibility value in 2010 (14.7), they are also the only group where susceptibility increases over time (but still remaining at a low level compared to other countries’ income groups). The low and low–middle income groups start with the highest susceptibility values (54 and 37), but the scenarios show a potential improvement in all scenarios in terms of susceptibility reduction. Particularly interesting are the different scenario results for upper middle income countries.

The upper middle income group starts with the second lowest susceptibility values (around 24.3) and shows a large improvement of susceptibility values for the “Sustainability First” scenario, whereas the “Security First” scenario shows minor improvements for the susceptibility values of those countries (see Figure S.3). The countries within this income group show the largest gap between the “Sustainability First” scenario and the “Security First” scenario. The development of susceptibility values for the low middle income countries started to spread around the year 2018 for all three scenarios, whereas the “Sustainability First” scenario shows the biggest improvement and the “Security First” scenario the lowest.

Figure S.3. Susceptibility (index value on the y-axis, max. value = 100) based on three different scenarios (“Base Case”, “Security First” and “Sustainability First”) using the methodology of the WorldRiskIndex. Above global average for susceptibility and below susceptibility related to four different World Bank income groups (high income, upper middle income, low middle income and low income).

S7 Description of the International Futures system (IFs model)

IFs is a software tool whose central purpose is to facilitate exploration of possible global futures through the creation and analysis of alternative scenarios. IFs is a large-scale, long-term global modelling system that incorporates and integrates modules of population, economics, education, health, infrastructure, energy, agriculture, the environment and socio-political change. Figure S.4 shows the major conceptual blocks of the IFs system.1

IFs represents the dynamic connections among all these systems for 186 interacting countries, drawing on standard approaches to modelling specific issue areas whenever possible, extending those as necessary, and integrating them across issue areas.2 Underlying the model is an extensive database of country-specific data for the issue areas drawn from the family of the United Nations member organisations and other sources covering the time period from 1960 to the present. The model itself can produce forecasts from its base year of 2010 as far as 2100. Most important, the forecasts it produces, although grounded in historical data, are not extrapolations, but rather represent the results of the dynamic interplay among variables in multiple domains of human development systems.

Fundamentally, IFs is a thinking tool for exploring human leverage in pursuit of key goals in the face of great uncertainty. IFs assists with:

understanding the state of the world and the future that appears to be unfolding by:o identifying tensions and inconsistencies that suggest political, economic, or

other risk in the near or middle term;o exploring long-term trends and considering where they might be taking us;o working through the complex dynamics of global systems;

thinking about the future we want to see by:o clarifying goals and priorities;o developing and exploring alternative scenarios (“if-then” analyses);o investigating what leverage we may have in shaping the future.

1 The technology components are embedded throughout the model; all the rest of the conceptual blocks are represented by specific modules and linked to other modules. The named linkages in Figure S.4 represent only a small illustrative subset of the dynamic connections between the block components.2 For example, the population model in IFs is based on a typical “cohort-component” representation, tracking country-specific populations and events (including births, deaths and migration) over time by age and sex; IFs then extends this representation by adding education and health.

S8 IFs model: Descriptions of the three scenarios

The IFs “Base Case” scenario is the output of the fully integrated IFs system. It is not a simple extrapolation of variables, but rather a dynamic, non-linear depiction of the future given the structure of the model and our “Base Case” assumptions about model parameters. The “Security First” and “Sustainability First” scenarios are drawn from the United Nations Environment Programme’s fourth Global Environmental Outlook (Rothman et al., 2007). For this study, the same parameter assumptions used to generate the quantitative output for GEO4 were applied, but with an updated version of the IFs system (v. 6.69).

In the “Security First” scenario governments and the private sector compete for control in efforts to improve, or at least maintain, human well-being for mainly the rich and powerful in society. “Security First”, which could also be described as “Me First”, has as its focus a minority: rich, national and regional. It emphasises sustainable development only in the context of maximising access to and use of the environment by the powerful. Contrary to the Brundtland doctrine of interconnected crises, responses under “Security First” reinforce the

Figure S.4. Major models in the International Futures (IFs) system and example linkages (Source: Rothman et al. 2007).

silos of management, and the United Nations’ role is viewed with suspicion, particularly by some rich and powerful segments of society.

In the “Sustainability First” scenario, governments, civil society and the private sector work collaboratively to improve the environment and human well-being, with a strong emphasis on equity. Equal weight is given to environmental and socio-economic policies, and accountability, transparency and legitimacy are stressed across all actors. As in Policy First, it brings the idealism of the Brundtland Commission to overhauling the environmental policy process at different levels, including strong efforts to implement the recommendations and agreements of the Rio Earth Summit, the World Summit on Sustainable Development and the Millennium Summit. Emphasis is placed on developing effective public-private sector partnerships not only in the context of projects but also that of governance, ensuring that stakeholders across the spectrum of the environment-development discourse provide strategic input to policymaking and implementation. There is an acknowledgement that these processes take time and that their impacts are likely to be more long-term than short-term.

S9 Case study Jakarta: Scenario methodology and background data

The qualitative scenario exercise was conducted as part of the 8th meeting of the UNU-EHS Expert Working Group on Measuring Vulnerability in Bogor (close to Jakarta), Indonesia, 12-16 July 2012. The meeting focused on the topic of “Development Pathways for Urban and Rural Coastal Zones” and was attended by national and local experts from Indonesia (Indonesian Institute of Sciences/LIPI, National Statistical Office/BPS, and Gadjah Mada University/UGM) and international experts (from France, Germany, USA, UK and Vietnam). Report on the meeting can be found in UNU website (http://www.ehs.unu.edu/article/read/9th-meeting-of-expert-working-group-on-measuring-vulnerability).

Within the case study of Jakarta the following steps in the scenario development were undertaken:

First the moderators defined the scenario space and formulated core questions. Related to that, the key themes (or axes) of the scenario space for Jakarta were outlined. The first axis juxtaposes scenarios of adaptive development with non-adaptive development in the context of climate change. The second axis was defined by local and national experts and practitioners in order to ensure that context specific discourses were sufficiently considered. This axis was defined by juxtaposing current development patterns and visions. As such, one vision is for the metropolitan area of Jakarta to become a high-tech city that is not resource-intensive, compared to the current conditions of a rather labour- and resource-intensive city.

Resulting from the two axes is a four-scenario space:

a. Adaptive but still labour-intensive and resource-intensive

b. Non-adaptive, still labour-intensive and resource-intensive

c. Adaptive, non-labour-intensive, high-skilled labour demand

d. Non-adaptive, non-labour-intensive, high-skilled labour demand.

The focus on four scenarios within a participatory process was helpful in order to avoid a too large number of alternative scenarios increasing the complexity and becoming unworkable for such an audience and group. The final selection of key trends and criteria for the four scenarios was conducted during the discussion of what the different scenarios might imply. That means the four potential futures illustrate major challenges but also opportunities for future development strategies, risk reduction and climate change adaptation at the same time.

Within the second phase, key trends that shaped the present situation of the city of Jakarta were explored. In each scenario the different indicators and their development trends were identified and discussed. Each scenario contains a similar set of indicators and criteria, however, their trends and directions (increasing, decreasing, better, and worse) differ significantly. For example, in a more adaptive scenario where Jakarta city remains a labour and resource intensive city, the environmental awareness is high but challenges still exist with regard to the resource-intensive industries. In contrast, with a non-adaptive, labour- and resource-intensive scenario, the environmental awareness is low (see Figure S.5). The identification of major trends that shaped the situation at present as well as the discussion of important trends that will influence future developments was an important tool to identify key indicators for such scenarios. Most of the indicators selected, such as the development of informal settlements/slums, migration, social conflicts, employment situation, transport problems or the quality of governance and the performance of natural hazard management (see Figure S.5) were seen as important trends that shape urban development. Interestingly, the four scenarios allow the experts and participants to discuss trends and indicators in relation to the other scenarios. Such communication focused on the differences between the four scenarios and proved helpful in increasing the analytic precision of the discussion (Figure S.5).

The discussion and the mapping of the indicators on a white board allowed a transparent and understandable scenario discussion. The documentation and visualisation was also helpful in the sense that contrasting alternative scenarios for Jakarta could be checked and critically reviewed by other participants (third phase of scenario construction).

S9.1 Background data – Jakarta Case Study

The following section describes the current conditions in Jakarta based on official statistical data available with regard to variables assessed in the qualitative scenario exercise. The available statistics cannot provide data for all variables; nevertheless, the description gives an overview of the state-of-the-art and trend of development pathways in Jakarta. The data analysis was enabled through provision of statistical data from BPS and further research. It was not part of the participatory scenario development, however, it is an ongoing task in the cooperation between LIPI and UNU-EHS.

S9.1a Population, migration and the issue of suburbanisationAs capital city of Indonesia, Jakarta is its most populous province. The population of Jakarta increased significantly in the period 1971 to 2010. In 1971, the population was only

4,579,300, but more than doubled by 2010 to 9,607,800, with an annual population growth of 1.42 per cent. In fact, this number exceeded the projection based on the 2005 Intercensal Population Survey (9,294,900). Large populations in large metropolitan cities like Jakarta can increase risk and vulnerability to climate change. According to the United Nations DESA report on World Urbanization Prospects, 2011 revision (United Nations, 2012), the population of Jakarta is projected to be as high as 12.8 million by 2025.

Five of six districts of Jakarta have been identified as the top most vulnerable regions in South-East Asia to climate change impacts (Yusuf and Francisco, 2010). The high level of vulnerability is largely due to their high population density. Based on the 2010 population census, the district with most residents is Jakarta Timur, but the most densely inhabited is Jakarta Pusat, while the highest population growth rate is in Jakarta Barat (see Table S.2).

Table S.2 Population, land area, population density and annual population growth by district, 2010

DistrictPopulation (thousand)

Land area (km2)

Population density (person/km2)

Annual population growth (2000-

2010)

Jakarta Selatan 2.06 141.27 14,597.81 1.46

Jakarta Timur 2.69 188.03 14,326.95 1.38

Jakarta Pusat 0.90 48.13 18,761.13 0.32

Jakarta Barat 2.28 129.54 17,615.76 1.83

Jakarta Utara 1.65 146.66 11,220.91 1.49

Kepulauan Seribu*

0.02 8.70 2,423.22 2.03

Jakarta 9.61 662.33 14,506.04 1.42

Source: The 2010 Population Census (BPS, 2010)

*This district has significantly different characteristics from the other districts, since it consists of several small islands with a rather rural profile, located to the north of the main Java island. The impact of sea-level rise may affect the district, but the impact of urban development of Jakarta may look different on this particular district.

In addition to natural increases, the increasing population in Jakarta is a result of in-migration from other regions of Indonesia. As a metropolitan city, Jakarta attracts many people to live there. Data from various censuses showed that the numbers of recent in-migrants fluctuated but the number was always about 700,000 people per year. In 2010, in-migrants to Jakarta reached more than 750,000 people, an increase of 7.7 per cent compared to the year 2000. Based on Badan Pusat Statistik (BPS), the Indonesian Government’s statistics agency, Intercensal Population Survey 2005 (BPS SUPAS, 2005), the main reason for in-migration to Jakarta is to work (61 per cent). In particular, many labourers came to work in the existing manufacturing industries. A slightly higher proportion of these migrants are women, who may work as factory labour, domestic help, babysitters, and elderly care workers. Generally they are migrants with low education and low socio-economic status and they come to Jakarta for a job and better living standards. Large numbers of such migrants are also indicated by slum areas in the city.

Despite increasing population numbers, the population growth rate of Jakarta has actually been declining. Over the period 1980 to 2000, the population growth in Jakarta has been falling and there has been a fairly high number of out-migrations from Jakarta. This reflects the spill-overs to adjacent areas, especially in Bogor, Tangerang and Bekasi (“Botabek”), due to suburbanisation in those areas (see e.g. Hata, 2003; Rustiadi et al., 2002; Firman, 2009). In the period 1990 to 1997, there was a rapid industrial development in the adjacent cities and districts of Jakarta, especially Tangerang, Bekasi, Serang and Karawang, which triggered further movement of population to these areas. In the period between 1995 and 2000, 190,000 people moved from Jakarta to Bekasi, 192,000 people to Tangerang, and 160,000 to Bogor and Depok (Firman, 2009). In 1997, a financial crisis also occurred in Asia and affected Indonesia, with Jakarta being severely affected (Firman, 1999). The economic

impact was especially felt in 1998 and was accompanied by political change (resignation of former President Suharto and the “reformation” era) and social conflicts. It may also have contributed to the high number of out-migrants from Jakarta in this period.

A handful of new residential developments in the form of “new towns” in the urban fringes of Jakarta only serve as settlements and are still socio-economically dependent from Jakarta, which then intensified daily interaction with Jakarta and contributed to more traffic and air pollution problems (Firman, 2009). According to a survey by the Central Bureau of Statistics (1992) conducted in 1991, about 96 per cent of the population lived or worked in Jakarta (Rustiadi et al., 2002), while the percentage of Botabek inhabitants working in Jakarta were 47.8 per cent for Bogor, 55.5 per cent for Tangerang and 59.8 per cent for Bekasi. The number of daily commuters is estimated to increase from 762,000 in 2000 to about 1.8 million in 2015 (JICA and BAPPENAS, 2001 cited in Susilo et al., 2007). Firman (2009) even suggested that the number of commuters between Jakarta and its surrounding areas reached 3 million in 2002. This implies that the assessment of development pathways in Jakarta needs to take into consideration the inter-linkages with these suburban areas and adjacent cities. The evidence of suburbanisation in the adjacent areas of Jakarta also emphasises the need to not only consider the population numbers (and growth) of Jakarta but also the daily dynamic of the population of mobile daily commuters from these areas that will be exposed to and affected by hazard events occurring in Jakarta.

S9.1b Poverty and slum areasAs with other fast-growing megacities in developing countries, Jakarta has been struggling with the issue of poverty. According to BPS data, the total number of poor people in Jakarta was 277,100 (3.18 per cent) in 2004 and increased to 388,700 (4.04 per cent) in 2010. These numbers vary slightly by district. Omitting Kepulauan Seribu, Jakarta Utara (the northern district at the coast) has the highest proportion of poor people (5.62 per cent).

The spatial distribution of poor people is to some extent reflected by the spatial distribution of the slum areas3. Figure S.5 shows the number of households living in slum areas at the village level based on PODES data (BPS PODES, 2008; 2011). Most slum areas occur in kelurahan (administrative villages) with industries and trade as the main source of income. There have been some positive and negative changes in slum areas within this period, also due to poverty alleviation and slum upgrading programmes by the government. However, we can identify that the most likely district exposed by sea-level rise, Jakarta Utara, has an increased number of households living in slum areas. Additionally, a significant correlation (Spearman coefficient 0.483, significant at p < 0.01) was identified between the number of households living in slum areas and the number of households living on the river banks at village level. This further emphasises the higher exposure of poor people to floods.

3 Based on PODES 2011 guidelines, slum areas are settlement areas with dense houses that are not adequate to be lived in, have insufficient sanitation, and are densely populated.

Number of households living in slum areas by Kelurahan (villages)Source: PODES data 2008 and 2011

2008 2011

Figure S.5. Distribution of households living in slum areas in 2008 and 2011. (Source: authors’ map based on BPS PODES data for 2008 and 2011).

S9.1c Social conflictsThe level of social conflicts may be indicated by the number of massive fighting incidents4 occurring in a year. Based on data from the 2011 village potential statistics (BPS PODES, 2011), there were 72 out of 267 administrative villages in Jakarta with massive fighting incidents. The types of most massive fighting incidents are between groups of people in a village (37.5 per cent), students (31.9 per cent), and among individuals/citizens (22.2 per cent).

S9.1d GovernanceAccording to governance survey in 2008 (Kemitraan online website, 2008), the top rank in the Partnership Governance Index is given to Special Capital Area (DKI) of Jakarta that has obtained a score of 6.51. This score belongs to the category of “fair”. It obtains a score of fair for nearly all the arenas of government (6.80), bureaucracy (7.34), civil society (6.31) and the economic society (4.64).

4 A massive fighting incident is defined as a fight in bulk that involves many actors such as residents, students, ethnic groups or others caused by mutual ridicule, misunderstanding, juvenile delinquency, old grudges, etc., during the past years.

For the Government and Bureaucracy, the DKI Jakarta Province obtains scores that range between fair and good in nearly all the principles. Both these state institutions are considered fair, accountable, transparent and effective. Within these two arenas the only aspects considered as problematic are participation: 3.20 for bureaucracy and 3.24 for efficiency in the arena of government. This means that the performance of the government and bureaucracy in DKI Jakarta Province are sufficiently fair, accountable, transparent and effective. However, the provincial bureaucracy has not completely accommodated public participation in their policy formulation. Forums such as the Participatory Development Planning (Musrenbang) are considered as yet incapable of accommodating public aspiration. Civil Society in the DKI Jakarta Province is considered as fairly good. This arena obtains good scores in the principles of accountability (7.84) and transparency (8.55); and fairness (5.50), effectiveness (5.94), participation (4.61) and efficiency (4.36). Economic Society in DKI Jakarta Province is not as good, with a poor score for Efficiency (1.00). This means that economic actors in the capital city of Jakarta are considered as inefficient in conducting business activities related to government projects.

S9.1.e EducationIn terms of social development in education, Jakarta has a good level of education and stable development. Based on BPS data, the adult literacy rate is very high (98.31 per cent in 2004 and 99.13 per cent in 2010), and average years of schooling were 10.4 in 2004 and 10.93 in 2010. Jakarta was the first province to initiate the 12 years of education compulsory programme. Comparing by district, Jakarta Utara and Jakarta Barat have relatively lower proportions of people with senior high school and higher education attainment.

S9.1.f UnemploymentAlthough labour intensive, Jakarta still has a high unemployment rate. According to the BPS National Labor Force Survey 2010 (BPS SAKERNAS, 2010), about 11 per cent of the population are unemployed, with a labour-force participation rate of 67.8 per cent.

S9.1.g TransportationJakarta is an area with high mobility, but due to the large population and its density, the capacity of the public transportation infrastructure has not been able to meet sufficiently the existing demand. Since the mid-1990s, the number of private motorised vehicles (motorcycles and cars) has been increasing significantly. Based on data for registered vehicles in 2002 (Susilo et al., 2007), there were more than 2 million motorcycles (about four times the number in 1985) and more than 1 million cars (about twice as many as in 1985), with the average of number of cars being 20.7 per 100 households. In contrast, the availability of public transportation vehicles such as buses has been rather low (Susilo et al., 2007). The emission problems due to intensive motorisation were recognised, and a mass transportation system has been planned for the near future, such as bus rapid transit (already implemented with quite positive preliminary impact) and monorail.

S9.1.h Environmental conditions and managementThe allocation of funding for environmental management of DKI Jakarta is still relatively low. In 2011, the total budget for environmental management was 27,593,622,000 Rupiahs (Hidayati, personal communication, August 2012), which was lower than in previous years.

S9.1.i Disaster events, impacts and preparednessFloods are the main concern in Jakarta with regard to natural hazards so far. Based on BPS PODES data (BPS PODES, 2008), there have always been areas affected by flooding each year from 2005 to 2008. The major flooding event occurred in 2007, which affected about 60 per cent of the area of Jakarta, especially in Jakarta Selatan, Jakarta Timur and Jakarta Utara. However, the level of disaster preparedness at the local level still varies and in some districts remains very low, e.g. in Jakarta Utara only about 25 per cent and in Jakarta Timur 36.9 per cent of villages have been socialised about disaster preparedness, while in Jakarta Selatan the proportion is 81.5 per cent, because of efforts by the provincial and national governments. The PODES data also indicated that support at the district, village and community level have made a significant proportion in the ongoing disaster preparedness activities.

S9.2 Participants of the Expert Working Group on Measuring Vulnerability – Topic: “Development Pathways for Urban and Rural Coastal Zones”, 12-16 July 2012, Bogor, Indonesia (the scenario development took place within a breakout group, particularly with participants from Indonesia)

Name Institution1 Prof. Jakob Rhyner UNU-EHS

2 Dr. Fabrice Renaud UNU-EHS

3 Dr. Joern Birkmann UNU-EHS

4 Mr. Matthias Garschagen UNU-EHS

5 Ms. Claudia Bach UNU-EHS

6 Dr. Akhilesh Surjan Kyoto University

7 Prof. Jan Sopaheluwakan Indonesian Institute of Sciences (LIPI)

8 Prof. Wahyoe S.Hantoro Indonesian Institute of Sciences (LIPI)

9 Dr. Dedi S.Adhuri Indonesian Institute of Sciences (LIPI)

10 Dr. Herryal Anwar Indonesian Institute for Sciences (LIPI)

11 Dr. Deny Hidajati Indonesian Institute for Sciences (LIPI)

12 Edi Prasetyo Utomo Indonesian Institute for Sciences (LIPI)

13Irina Rafliana Indonesian Institute for Sciences (LIPI)

14 Ms. Siswani Sari Planning and Development Agency (Bappeda) Kota Sabang, Aceh, Indonesia

15Dr. Bach Tan Sinh National Institute for Science and Technology

Policy and Strategy Studies, Vietnam

16Mr. Nguyen Quang UN-Habitat Asia

17

Ms. Tiodora Siagian Badan Pusat Statistik Republik Indonesia (Statistics Indonesia of The Republic Indonesia), Jakarta, Indonesia

18Ms. Novi Widyaningrum Center for Population and Policy studies

Gadjah Mada University (UGM), Indonesia

19Mr. Djati Mardiatno Center for Disaster Studies Gadjah Mada

University (UGM), Indonesia

20 Arisetiarso Soemodinoto The Nature Conservancy, Indonesia

21 Dr. Bas van Ruijven National Center for Atmospheric Research, Boulder, USA

22Dr. Hassan Virji International START, USA

23 Mr. Terry Cannon Institute of Development Studies, UK

24Prof. Günter Strunz German Aerospace Center (DLR), Germany

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