A methodological framework for rapidly assessing the impacts of climate risk on national-level food...

Global Environmental Change xxx (2014) xxx–xxx

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JGEC-1210; No. of Pages 12

A methodological framework for rapidly assessing the impactsof climate risk on national-level food security through avulnerability index

P.K. Krishnamurthy a,*, K. Lewis b, R.J. Choularton a

a Office for Climate Change, Environment, and Disaster Risk Reduction, United Nations World Food Programme, Via C.G. Viola 68/70, Rome, 00148, Italyb UK Met Office Hadley Centre, Fitzroy Road, Exeter, EX1 3PB, United Kingdom

A R T I C L E I N F O

Article history:

Received 16 November 2012

Received in revised form 18 August 2013

Accepted 6 November 2013

Keywords:

Vulnerability index

Food security

Climate change

Adaptation

Disaster risk reduction

A B S T R A C T

This article describes an experimental Hunger and Climate Vulnerability Index showing the relative

vulnerability of food insecure populations to climate risks at country level, as a tool for better

understanding risks to food security presented by climate change. Data from socioeconomic and

environmental indicators were analysed, and the most relevant indicators were aggregated using a

composite index to compare differential vulnerabilities. The paper shows the high correlation between

hunger and climate risk, especially for the regions of the world most affected by food insecurity. The

analysis goes beyond the impact of climate on crop yields and provides a multidimensional analysis of

vulnerability, while demonstrating the critical role that adaptive capacity has in determining

vulnerability. The paper also presents a method for analysing food security vulnerability to climate

risks that is replicable at different scales to provide a robust planning tool for policy makers. This

approach can also be used to monitor vulnerability, evaluate potential effectiveness of programmes, and/

or examine plausible impacts of climate change by introducing scenarios into the vulnerability model.

� 2013 Elsevier Ltd. All rights reserved.

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jo ur n al h o mep ag e: www .e lsev ier . co m / loc ate /g lo envc h a

1. Introduction

Food insecurity remains one of the key risks to development.Over eight hundred million people are currently undernourished(FAO/IFAD/WFP, 2012). Climate change has the potential toincrease the number of people at risk of hunger by 10–20percent—with the majority being in sub-Saharan Africa (Parryet al., 2009; FAO/WFP, 2010; Godfray et al., 2010; Beddington et al.,2012). Coupled with population and environmental pressures,climate change will exacerbate existing risks to food and livelihoodsecurity through a combination of interrelated factors: increasingfrequency and intensity of some extreme weather events (Burkeand Brown, 2008; Knutson et al., 2010; Pall et al., 2011),diminishing agricultural yields in the most food insecure areas(Lobell et al., 2011; Roudier et al., 2011), and exacerbating land andwater scarcity (Wani et al., 2009).

To date, the majority of studies of climate impacts on foodsecurity have focused on crop models, indicating where yieldsmight increase or decrease due to global warming (e.g. Rosenzweigand Parry, 1994; Arnell et al., 2001; Fischer et al., 2002; Parry et al.,2004). These studies generally agree that wheat, rice and maize

* Corresponding author.

E-mail address: [email protected] (P.K. Krishnamurthy).

Please cite this article in press as: Krishnamurthy, P.K., et al., A methorisk on national-level food security through a vulnerability indj.gloenvcha.2013.11.004

0959-3780/$ – see front matter � 2013 Elsevier Ltd. All rights reserved.

http://dx.doi.org/10.1016/j.gloenvcha.2013.11.004

yield decreases will be greatest in developing countries, therebysuggesting that food insecurity will be greatest in these. However,the models used in these analyses do not incorporate adaptivecapacities, ignoring the fact that people develop coping mecha-nisms to deal with shocks ranging from early warning systems tomigration (Johnson and Krishnamurthy, 2010). Moreover, focusingsolely on crop yield change excludes the important issue of foodaccess especially among market-dependent, food insecure house-holds. In order to systematically analyse the impacts of climatechange on food security, it is important to integrate a range ofrelevant factors that contribute to vulnerability, including expo-sure, sensitivity and adaptive capacity. In doing so, it is alsoimportant to link the components of food security (availability,access, stability, and utilisation) in their response to climatechange.

At the 16th Conference of the Parties to the United NationsFramework Convention on Climate Change (COP16) in Cancun andconsequent climate negotiations, a discussion emerged on theneed to define transparent methods to identify who is the mostvulnerable to climate risks. How vulnerable a country ultimatelydepends on how vulnerability is conceptualised, quantified andcommunicated—therefore, it is crucial to define to what a countryis vulnerable and in what timescale. Discussions on vulnerabilityhave highlighted that vulnerabilities are very contextual: the leastdeveloped countries (LDCs) are the least able to adapt to climate

dological framework for rapidly assessing the impacts of climateex. Global Environ. Change (2014), http://dx.doi.org/10.1016/


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Data collec tion

Data were collec ted fr om statistica l databases

Data process ing

Statistica l relationships were a ssessed

Ind ica tors were normalised

Selec tion of ind icato rs

Ind ica tors were selec ted ba sed on the ir relevance to food securit y

Calculat ionVulne rab ilit y= f(Exposu re, Sensiti vit y, Adapative Capacity)

Fig. 1. Methodological process.

P.K. Krishnamurthy et al. / Global Environmental Change xxx (2014) xxx–xxx2

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change and are therefore highly vulnerable (due to immediate risksthat could prolong over the longer run, including a range ofclimate-related hazards such as droughts and floods); small islanddeveloping states (SIDS) are extremely vulnerable due to the highexposure and low elevation (the risks are more long-term, andrelate to sea-level rise and coastal inundation which could result incomplete loss of territory); and South Asian countries areextremely vulnerable due to high population density in vulnerablesettings (resulting in high exposure to climate related risks such asstorm surges, coastal inundation and sea-level rise).

In the context of the challenges of developing a method toassess vulnerability, the paper suggests an experimental Hungerand Climate Vulnerability Index that has been developed jointly bythe United Nations World Food Programme and the UK Met OfficeHadley Centre. By aggregating readily available global datasets fordifferent socioeconomic and environmental indicators, the pro-posed index can contribute to the debate on ‘who is the most

vulnerable to climate change?’ and provide inputs to identify thecountries that are most vulnerable to climate impacts in thecontext of food security. Through a systematic quantification ofvulnerabilities, it is also possible to provide a more multidimen-sional analysis of the specific factors that render a countryvulnerable to the adverse impacts of climate change. The Hungerand Climate Vulnerability Index presented here provides acountry-level analysis of food security and climate risk to informplanning. This approach can be replicated at sub-national levels toidentify and evaluate context-specific risks, and can also be used tomonitor vulnerability trends, evaluate potential effectiveness ofprogrammes, and/or examine plausible impacts of climate changeby introducing climate projections and adaptation scenarios intothe vulnerability index model.

2. Analytical framework

Vulnerability arises from the complex interactions betweensocio-economic, institutional and environmental systems whichcomplicates any assessment or quantification. Nevertheless,exercises to quantify vulnerability are important to help in theprioritisation and planning of activities, including adaptation to achanging climate.

For the purposes of this paper, vulnerability refers broadly tothe climatic conditions (such as rainfall variation, temperaturechanges, and extreme hydrometeorological events) that increasethe food security risks faced by households or communities in caseof a shock. Vulnerability is therefore a function of how households(or communities, or countries, or regions) are affected to adverseconsequences (injury, damage and/or loss) as a result of a shock,and how they cope with this exposure (cf. WFP, 2009a,b).

The Intergovernmental Panel on Climate Change (IPCC WGII,2007) further defines vulnerability to climate change as theinteraction between three components:

(i) The magnitude and duration of climate-related exposure;(ii) The sensitivity of a target system (in this case, food security) to

climate risk; and(iii) The ability of a system to withstand or recover from the

exposure (adaptive capacity).

Therefore, for the purposes of this analysis, food security isconsidered to be vulnerable through the interaction of exposure toclimate-related shocks, agricultural and environmental sensitivi-ties and the capacity to deal with climate risks. This paper does notaim to review the whole range of links between climate risk andfood insecurity, but a summary is presented below. For a detailedreview of these relationships, please see Gregory et al. (2005),Schmidhuber and Tubiello (2007), Lobell et al. (2008), Brown and


Funk (2008), Gornall et al. (2010), Godfray et al. (2010),Beddington et al. (2012), UK Met Office Hadley Centre and WFP(2013).

Climate change could result in increasing magnitude of extremeweather events such as floods, droughts, and storms (cf. Pall et al.,2011; IPCC, 2012), which could in turn increase the exposure offood insecure communities. Higher frequency, intensity, orduration of extreme weather could affect food production (Lobellet al., 2011), reduce agricultural production in some areas (Nelsonet al., 2010), food prices Nelson et al. (2009), the ability to accessmarkets (Ericksen et al., 2011), and nutrition outcomes (Rodri-guez-Llanes et al., 2011). In addition, long-term changes in climatecould affect the suitability of land for crop production as well as thesustainability of rain-fed agriculture in some areas (Schmidhuberand Tubiello, 2007). Therefore, climate change could increaseexposure in complex ways.

Climate change could also increase the sensitivity of foodinsecure households. The majority of agriculture in the most foodinsecure countries is rain-fed (Wani et al., 2009), and as climatechange affects weather patterns this could affect the suitability ofagricultural production regions for crop production, therebyaffecting crop yields as well. The availability of water could alsoaffect crop yields–changes in rainfall patterns under climate changecould affect water availability and therefore exacerbate agriculturalsensitivities. Temperature and soil moisture are also importantfactors that will alter under climate change, potentially increasingthe sensitivity of food insecure households. Temperature couldaffect water and soil moisture requirements, and therefore affectagricultural sensitivity (Schmidhuber and Tubiello, 2007).

Finally, the extent to which climate variability, extremes andchange affect food security depends on the extent to which at-riskcommunities and countries can respond to the impacts. Thestrategies used to manage and adapt to climate risk are highlycontextual (IPCC WGII, 2007), and it is beyond the aims of thispaper to explain how different adaptation strategies can reducevulnerability to climate change and increase food security (for anoverview of the role of climate change adaptation in supportingfood security outcomes see, for example, Burke and Lobell, 2010;WFP, 2011).

3. Method

The method used for this paper consisted of a four-step processin order to develop the Hunger and Climate Vulnerability Index(Fig. 1).

First, an in-depth literature review guided by the conceptualframework outlined above was used to identify relevant data setsfor consideration (see Appendix A for a list of selected indicatorsand rationale for inclusion). The datasets were obtained and




Table 1Summary of components and indicators used for the vulnerability index including statistical relationship between indicators and undernourishment.

Component Input Statistical relationship Data source

Profile Indicator Correlation to

hunger (p-value)

Exposure (r = 0.511) Climate hazard Mortality (per 100,000 population) 0.611 (<0.05) IMF

Reported economic losses per capita (% of GDP) 0.212 (<0.05) IMF

Number of droughts (2000–2010) (unit) 0.412 (<0.05) EM-DAT

Number of floods (2000–2010) (unit) 0.191 (<0.05) EM-DAT

Number of storms (2000–2010) (unit) 0.134 (<0.05) EM-DAT

Sensitivity (r = 0.623) Environment Forest cover (% of total area) �0.299 (0.09) World Bank Data

Agriculture Rainfed agriculture (% of total agriculture) 0.143 (<0.05) World Resources Institute

Cereal crop production (yield/ha) �0.611 (<0.05) FAOSTAT

Low elevation

coastal zones

Population in LECZ (% of total) 0.091 (0.07) CIESIN

Rural area in LECZ (% of total) 0.311 (0.06) CIESIN

Adaptive capacity (r = �0.791) Infrastructure Water access (rural population) (%) �0.600 (<0.05) World Bank Data

Water access (urban population) (%) �0.411 (0.07) World Bank Data

Paved roads (% of all roads) �0.422 (0.08) World Bank Data

Socioeconomic structure Decadal population growth (2000–2010) (%) 0.511 (<0.05) UNFPA

Total population below poverty line

($2 per day, PPP) (%)

0.799 (<0.05) UNDP

Vulnerable employment (%) 0.523 (0.07) World Bank Data

Rural population (%) 0.621 (<0.05) World Bank Data

Governance Government effectiveness �0.711 (<0.05) World Bank Governance Data

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reviewed for quality and completeness. Data were collected fromWorld Bank, World Resources Institute (WRI), EM-DAT, ColumbiaUniversity’s Centre for International Earth Science InformationNetwork (CIESIN) and United Nations agencies (FAOSTAT, UNFPA,IMF) global databases. An indicator is defined as a variable thatquantifies and characterises the level of vulnerability or resiliencein a country. The index is consequently a composite of theindicators that summarises overall vulnerability to climateimpacts.

Second, a statistical analysis was carried out to determine thecorrelation between specific indicators and undernourishment.While no international guidelines for measuring hunger exist(Barrett, 2010), undernourishment is used as a proxy for hungerhere. Undernourishment is the most widely collected character-istic of hunger at the global level, and is quantified by measuringthe proportion of kilocalories available, based on total cropproduction, divided by population numbers (FAO, 2010). Becausethese measurements are routinely included in national censuses,this is the most comprehensive global dataset for hunger over longtimeframes (FAO, 2010; also Carlson et al., 1999). This measure-ment is primarily useful for quantifying long-term effects ofmalnutrition, rather than cases of acute food insecurity followinga food security crisis. For this analysis, undernourishmentstatistics were considered to be the most feasible benchmark toquantify vulnerability given the availability of global data for thisindicator. However, in future research, other food securityindicators such as dietary diversity, diet quantity, food pricevolatility, and middle upper arm circumference can provide moreinformation about the links between climate risk and specificaspects of food security.

Third, indicators were selected based on the results of thestatistical analysis. Multiple indicators were selected to representeach of the three major components of vulnerability (exposure,sensitivity and adaptive capacity). If no statistically significantrelationship could be ascertained between a candidate indicatorand hunger, the indicator was removed from consideration. Theindicators were also tested for autocorrelation; where two or moreindicators with a statistically significant relationship with hungerwere autocorrelated, only the indicator with the highest correla-tion to undernourishment was included in the index. Table 1summarises the statistical relationship between the selected


indicators and undernourishment. A full list of indicators consid-ered for the index is presented in Annex 2.

Fourth, selected indicators were used to calculate the Hungerand Climate Vulnerability Index as outlined below. The results ofthe calculation were mapped on a Geographic Information System(ArcMap 9.3) to determine the geographical distribution ofvulnerability; the results were subsequently compared withstatistical results at the global level.

A balanced weight approach (Sullivan, 2002; Hahn et al., 2009)is used in this index, assuming that each component contributesequally to the index, regardless of the number of indicators undereach component. To do this, the component score was divided bythe number of indicators. The weighting scheme can be adjusted toreflect the perceived importance of specific factors. For example,Eakin and Bojorquez-Tapia (2008) suggest discussions with focusgroups and expert opinion to determine the weightings ofindicators.

Each indicator was measured on a different scale (or differentunits), so it was necessary to standardise them. In order to carry outthe conversion, the values were indexed and represented as apercentage of the maximum for that indicator:

Indicatorstandardised ¼Indicatorvalue

Indicatormaximum

Subsequently the indicator values were added to obtain thevalue of the component:

Component value ¼ Indicator1 þ Indicator2 þ . . . þ Indicatorn

n

where n is the number of indicators for a particular component.Adaptive capacity reduces vulnerability so it contributes

negatively to the index. To account for this, the inverse value isused (1 � x): this value is used for the remainder of this paperwhen referring to adaptive capacity and should be interpreted as alack thereof.

Component values are similarly normalised such that themaximum value for each is 1. The resulting normalised values werethen multiplied to obtain the index score:

Vulnerability score ¼ Exposurevalue � Adapative capacityvalue




Table 2Criteria for vulnerability to climate risk.

Vulnerability

index

<0.2 0.2–0.4 0.4–0.6 0.6–0.8 >0.8

Severity of

vulnerability

Very low Low Medium High Very high


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The index score was also normalised with 1 being themaximum value. The results of the calculation are used to expressrelative vulnerability at the country level. Values associated withthe model were divided into five different categories using thequintile method, with each range cumulatively representing 20% ofthe maximum vulnerability (Table 2).

This paper argues that one of the advantages of the index is thatit was calculated relatively rapidly. The entire process fromliterature review, data collection and processing, and calculation ofthe index took approximately 6 weeks. Compared to othervulnerability assessments – at global, national, or sub-nationalscale –, this is relatively quick. By using readily available data, it ispossible to make quick assessments of the types of risks that mustbe considered in the adaptation planning process.

The index can be replicated at smaller scales by following theapproach presented here: a literature review to identify key issuesand potential indicators, collection of indicators at the desired levelof resolution, statistical analysis to determine correlationsbetween given indicators and food security, selection of indicators,aggregation of indicators, and calculation of the index. Analysis athigher resolution is likely to provide more robust results foradaptation planning (cf. Barnett et al., 2008).

In addition the simplicity and transparency of the index makesit a useful tool for exploring the uncertainty in the methodology,both from the assumptions within the index itself and thatintroduced when projections of climate change are considered. Theindex can be re-calculated using future model projectionsrelatively easily, and with the range of projections available frommodelling centres globally, this means that it would be possible toexplore the sensitivity of the vulnerability index to climate change,through an ensemble-based approach. Similarly, sensitivity toadaptation responses can be tested, either by introducing differentindicators that may be more appropriate at other scales, or byintroducing scenarios of future indicator ranges.

3.1. Limitations of the vulnerability index

Due to the scale (country-level) of the datasets used for thisindex, the analysis has a coarse spatial resolution, potentiallyhiding local vulnerabilities. While the method for developing thisindex can be applied using sub-national socioeconomic data, at theglobal scale the analysis should only be used to understand globaland regional patterns of vulnerability. The advantage of looking atfood security at the global scale is that it allow comparisonbetween countries, and an understanding of the relative impacts ofclimate change to be understood. However, for adaptationplanning, high resolution analysis is required.

To overcome the limitations of the coarse resolution, it isimportant to carry out the analysis at higher level of detail. Forexample, for country-level analyses, it is possible to use nationaldatabases (census, geospatial, meteorological) whereas for com-munities it is possible to use field survey data. Using context-specific data at national or sub-national levels will show differentrelationships between undernourishment and exposure, sensitivi-ty and adaptive capacity indicators, but is also very likely toprovide a more robust explanation of local vulnerabilities. Carryingout the analysis at sub-national level would provide a moreaccurate representation of factors affecting vulnerability in that


particular context, and can provide useful information to be able tocompare countries with different attributes. For example, exposureto sea-level rise is a key factor contributing to food insecurity inisland nations and areas with high agricultural production alongthe coastline, but may not be directly relevant in land-lockedcountries like the Democratic Republic of Congo (which currentlyhas one of the highest FAO undernourishment rates).

When replicating this analysis it will also be important toexaminethe relationship between food security and transnational processes.Market flows can sometimes be influenced by climate trends and arecritically important for food security, especially among pastoralistcommunities. For example, excess rainfall may increase theprobability of livestock contracting vector-borne diseases, whichin turn could affect livestock quality (including meat and milkproducts) as well as terms of trade with pastoralists in othercountries. Similarly, the effects of remote climate are not captured inthe current index. In Bangladesh, for example, the incidence of floodsand droughts depend strongly on India’s water managementupstream of the Ganges water system, which is in turn sensitive toHimalayan snow melt. These transnational issues are not currentlycaptured in the index but additional contextual analyses may becarried out to provide more information for decision-making.

The index uses readily available data which are updatedregularly. An advance of using such data is that the index can becalculated and updated rapidly without having to collect the data(which can often be an expensive exercise), and the method istransparent and repeatable. However, this means that because theindex relies on publicly available data, it is difficult to adequatelyassess sources of data error and account for those in the index.Moreover, given that some data are not publicly available, theindex may not incorporate some key factors (such as nutritionaldata)—these may need to be collected or derived separately.

Additionally, different countries have different reportingcapacities and standards for monitoring hazards. For example,the EM-DAT database is used for this study as it provides astandard database for disasters based on at least one of thefollowing criteria: 10 or more people killed, 100 or more peopleaffected, declaration of a state of emergency, and/or a call ofinternational assistance. However, the database relies on thereporting capacities of countries and measures hazards reportedby impact. This does mean that for some of the most vulnerablecountries the index might not fully express this vulnerability, ifreporting capacities are low. In future assessments of vulnerability,it would be useful to evaluate exposure through an analysis ofobservational meteorological records or re-analysis data recreat-ing past weather conditions through a hybrid of modelledatmospheric conditions constrained by observations.

While quantitative empirical studies such as the one carried outfor this paper cannot directly attribute directions of causality, theanalysis assumes that the selected socioeconomic and environ-mental indicators cause food insecurity. In reality, however, therelationships between undernourishment and vulnerability arehighly complex and it is possible that the indicators selected hereare influenced by undernourishment.

The paper also assumes that measurement errors in globalsocioeconomic and environmental data are independent of foodsecurity, and therefore do not bias the interpretation of results.While the selected indicators might be characteristic of otherunderlying vulnerabilities which in turn influence food security,for simplicity, we assume that they influence food insecurity eitherdirectly or indirectly.

4. Results and interpretation

The following section summarises the results of the vulnera-bility indexing exercise. The results of the index were mapped on a




Fig. 2. Map showing climate and hunger vulnerability scores.


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Geographical Information System at the global level (Fig. 2). Themap can be interpreted as a baseline showing the vulnerability ofcountries’ food security to climate risk, relative to each other, basedon current climate and socioeconomic profiles. Index values shouldbe interpreted as relative, rather than indicative, values to becompared within the framework of the analysis.

The results suggest that there are geographical patterns of foodinsecurity and climate-related vulnerability (Table 3). Thissuggests two important observations which may need furtherempirical analysis. First, hunger and climate risk are interlinked:the most food insecure countries are also the most vulnerable toclimate risk. Second, climate change could exacerbate the risk ofhunger. Evidence collected from different contexts suggests thatclimate risk already affects food security in some regions. In 2011,in Eastern Africa, droughts associated with changing precipitationpatterns have threatened the lives of over 9 million people (FEWSNET, 2011); forecasts of poor rainfall in parts of Ethiopia, Somaliaand Kenya suggest that millions of livelihoods could continue to beat risk (FEWS NET, 2012). In a study of drought risk in Kenya,observed increases in local, seasonal temperature of around onedegree Centigrade and the decline in precipitation during the keyrainfall season have been linked to higher drought magnitude inKenya (Williams and Funk, 2010). If this trend continues, foodsecurity would be affected due to lower prime arable landavailable, which could affect critical surplus maize growing areasin Central Kenya (ibid.). These examples illustrate the closerelationship between climatic variability and food security inhighly vulnerable countries.

Table 3List of most vulnerable countries.

Hunger and climate vulnerability index

Country Region Index score

Bangladesh South Asia 1

Afghanistan South Asia 0.956

Chad Sub-Saharan Africa 0.939

Madagascar Sub-Saharan Africa 0.914

Somalia Sub-Saharan Africa 0.906

Burundi Sub-Saharan Africa 0.873

Yemen Middle East and North Africa 0.868

Niger Sub-Saharan Africa 0.867

Pakistan South Asia 0.854

Nigeria Sub-Saharan Africa 0.845

Burkina Faso Sub-Saharan Africa 0.829

Kenya Sub-Saharan Africa 0.828

Mali Sub-Saharan Africa 0.827

Ethiopia Sub-Saharan Africa 0.826

Haiti Latin America and the Caribbean 0.811


The distribution of vulnerability index scores is summarisedthrough a kernel density plot (Fig. 3). Vulnerability has a relatively‘‘normal’’ distribution such that the average vulnerability categoryis medium (0.525), but the distribution shows that a slightmajority of countries have above average vulnerabilities and only afew countries have very low vulnerability. The results of thevulnerability index show that:

(i) the most common vulnerability category is medium (0.4 �Vulnerability � 0.6);

(ii) few countries have very low vulnerabilities (Vulnerability �0.2);

(iii) the majority of countries have above-average vulnerabilities(Vulnerability � 0.525).

This implies that in a majority of countries food security isalready vulnerable to the impacts of climate-related risks,especially in sub-Saharan Africa and Asia.

The geographical and statistical distributions of the specificindicators shown in Fig. 4 further reveal that the majority ofcountries have low exposure and sensitivity values, but relativelyhigh (inverse) adaptive capacity values. The top map highlights thedistribution of adaptive capacity: countries in Western, Centraland Eastern Africa, and Central and Southern Asia have the lowestreported values and generally correspond to the highest vulnera-bility scores.

Fig. 3. Kernel density plot highlighting the distribution of vulnerability index scores.




Fig. 4. Maps and kernel plots showing the distribution of the vulnerability components: adaptive capacity (‘‘inverse’’) (top), sensitivity (middle) and exposure (bottom).


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The statistical analysis further indicates that adaptive capacitycorrelates closely with undernourishment (r = �0.791, p < 0.05).This suggests that increasing adaptive capacity (governmenteffectiveness, access to infrastructure and markets, povertyreduction) might yield the highest benefits for food security underclimate change. These results corroborate other studies whichhighlight the crucial contribution of poverty and socioeconomicfactors to vulnerability in the context of climate change (e.g. Kellyand Adger, 2000; ISDR, 2009, 2011).

Fig. 5. Vulnerability triangle for Bangladesh.


Sensitivity has the second highest correlation to undernourish-ment (r = 0.623, p < 0.05). The middle map highlights thegeographical distribution of sensitivity, and shows that the mostsensitive countries are in Northern Africa, the Middle East, andCentral and Southern Asia. This could be attributed to low forestcover (especially in desert environments) and high reliance onrainfed agriculture (such as the monsoon season in South Asia). Thestatistical distribution of sensitivity, as shown by the kerneldensity plot, has a rather ‘‘normal’’ distribution with mostcountries having below-average values.

Finally, the distribution of exposure is shown in the bottommap. Exposure has the lowest correlation to undernourishment(r = 0.511, p < 0.05). Indeed, few countries have very high exposurevalues (these are mostly located in South and East Asia). The resultsshow that, generally, larger countries report higher incidence andlosses from disaster because more land is potentially exposed;larger, richer countries are also more exposed in the sense thatthere is greater likelihood for loss. Importantly, the relatively lowvalues for exposure could suggest that vulnerability is moredependent on adaptive capacity and sensitivity—especially incountries with high undernourishment rates.

4.1. Applications of the vulnerability index

While the Hunger and Climate Vulnerability Index haslimitations in terms of providing specific analysis at sub-nationallevel, it can be used for comparative analysis as well asunderstanding the relative importance of different indicatorsincluded in the index at the national level. This type of analysisprovides an initial overview of the factors that render a countryvulnerable to climate risk.




Fig. 6. Spider diagrams showing the contribution of indicators to vulnerability.


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To illustrate this potential use of the index, Fig. 5 shows avulnerability triangle which plots the contributing factors forexposure, sensitivity and adaptive capacity for Bangladesh. Thediagram shows thatBangladesh is highly sensitive: indeed, 62% of thecountry’s agriculture is rain-fed and only 6.7% of the country isforested.

The vulnerability index could be used to assess the potentialimpact of a policy, programme or project by substituting the valuethat is expected to change and recalculating the index. Forexample, if an intervention aims to improve access to socialprotection, the new expected percentage can be inserted in theequation to calculate the new vulnerability score. To illustrate, inBangladesh, reducing vulnerable employment (and thereforeincreasing access to social protection) by 50 per cent, wouldincrease overall adaptive capacity by approximately 20 per centwhich would reduce overall vulnerability by approximately 7 percent compared to the baseline. However, the actual direct andindirect effects of any policy intervention are difficult to measureand would need a separate evaluation process.

Alternatively, in guiding policy, the index can be used toidentify the factors that are contributing the most to vulnerability.As an illustrative example, the case of Bangladesh is taken. Spiderdiagrams for all of the indicators (by component) are shown inFig. 6. The results show that the most important factorscontributing to vulnerability are vulnerable employment (ameasurement of the proportion of the labour force with no formalarrangements and no access to social protection), and lack of forest,suggesting that policies to address these issues would yield thehighest benefits in reducing vulnerability.

The index can be replicated at different temporal and spatialscales to provide detailed information about vulnerability in veryspecific contexts. At the national and sub-national levels, the indexcan be formulated using a similar approach that is based on areview of the literature on climate and vulnerability science,collection of relevant national datasets and a statistical analysis.Replication of the study under different national settings is likely togenerate vulnerability indices that differ from the one presentedhere, and that reflect the relevance of indicators for a particularcountry. Weightings can also be attributed to indicators based oninputs from experts and communities to further highlight theimportance of specific factors.

In order to improve the predictive value of specific indicators atthe national and sub-national level, a future vulnerability indexcould be based on an analysis of the historical relationshipbetween food security and different indicators, where an historicalrecord is available. For example, snow cover—not included in thisindex—is a highly location-specific factor which can influence foodsecurity by affecting the amount of water available for agricultureand might be less predictive in desert environments. Such ananalysis would highlight the indicators that are most representa-tive of overall vulnerability in specific countries, and wouldprovide more detailed information about the types of responsesneeded to address vulnerability.

Additionally, the index can be used to estimate the impact ofclimate; for instance, if the number of droughts in a country isexpected to increase under a particular climate change scenario,this value can be modified and the new vulnerability score can bere-calculated. In this context, climate models can be an importantinput to the index: by predicting the expected occurrence ofclimate-related events, as well as their associated economic lossesand mortality under different scenarios it is possible to understandthe impact of climate on vulnerability.

Incorporation of climate research is also important to identifyspecific climate-related events that are relevant at different spatialand temporal scales. For example, as information becomesavailable, the index might be improved at the regional scale by


including information about emerging risks such as glacial meltand its influence on river flow, or further data might be included onsea-level rise and its impact on coastal inundation across longertimescales.

Other specific impacts of climate change, such as changes inyield production, might be inputted into the vulnerability index toproject future vulnerabilities. Further analyses might also generateinformation about which areas are likely to be most affected byphysical impacts of climate change such as erratic rainfall. Bycorrelating modelled changes in rainfall patterns with areasdependent on rain-fed agriculture for production, it is possibleto identify further climate-related agricultural sensitivities.Further integration of climate science is needed for a robustmethod to guide policymaking.

4.2. Determining the appropriate scale for analysis

An important issue in the vulnerability assessment concernsthe scale at which the analysis is carried out. Indices that measurevulnerability to environmental change (including climatic change)





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at the global level may hide regional trends and are therefore moreuseful for smaller-scale analyses. The choice of scale depends onthe purpose of the analysis: country level (DARA, 2012),community level (Huang et al., 2012), household level (Notenbaertet al., 2012). This research used global datasets to comparevulnerabilities across the globe and to generate an overall pictureof current vulnerabilities of food insecurity to climate risks.

Future research might replicate this approach at national orsub-national level—this type of analysis would be helpful inguiding robust food security interventions with a climate changeadaptation component and can provide an important analyticalinput into planning and decision making.

Regional influences on climate variability must also beconsidered when determining the scale of the analysis: in theirhousehold level vulnerability assessment, Eakin and Bojorquez-Tapia (2008) note that that vulnerabilities should be analysed inthe context of larger social, economic and environmentalprocesses. For the index presented here, for example, disasterincidence data are more relevant at national and regional levelthan at the local level.

An additional factor that might determine the scale for analysisrelates to the availability of resources to collect, analyse andinterpret the results. The index proposed in this paper aggregatedopen source data from international datasets. Future researchmight replicate this approach using data collected specifically forfood security and climate vulnerability assessments.

Where robust climate science is available, future researchmight incorporate climate models into the vulnerability index toguide interventions. By applying climate projections into theindex, users could quantify the impact on exposure indicators—andthe relative impact on overall vulnerability. Future iterations of theindex could also allow users to manipulate the other indicators ofthe index to run sensitivity tests that can inform adaptationplanning. This option would allow users to explore the challengesof climate change in the face of uncertainty.

5. Conclusion

This paper suggests a Hunger and Climate Vulnerability Indexto assess the relative vulnerability of food security to climateimpacts at country level. The approach, based on a literature

Appendix A. Rationale for selected indicators

Component Indicator Rationale

Exposure Deaths per 100,000 inhabitants

(climate-related disasters)

Death is one of the most dir

provides a proxy of the relat

climate risks, the higher th

Nations, 2010).

Losses per GDP in %

(climate-related disasters)

Economic losses are another

in richer countries (with m

comparisons on the cumula

Greater proportional econo

Number of droughts Meteorological droughts (re

and intensity. Droughts res

2006). Globally, the areas so

seen an increase in the per

percent to approximately 12

already affected by drought

growth (UNEP/EEG and UN

Number of floods Food security may be affect

flood events is less certain.

suggest that there will be m

destroy entire crops over wi

crop land (due to sediment


review and complemented by rigorous statistical analysis,provides a detailed description of factors that contribute toincreased risk of food insecurity under climate change at the globallevel. Additional information is provided through visual repre-sentations of vulnerability, using triangle and spider diagrams thathighlight the overall vulnerability profile and the contribution ofdifferent indicators to vulnerability, respectively.

The study can be replicated at different spatial and temporalscales using context-specific datasets and climate scenarios.Replication of this approach is likely to provide differentvulnerability indices and might provide information about thedifferent contexts of exposure, adaptive capacity and sensitivity ofcountries over time. Emerging climate-related risks, such as glaciermelt and sea-level rise, might also be included in a vulnerabilityassessment as the science develops and data become available.

One of the findings of this analysis is that, at the global level,adaptive capacity and sensitivity have a higher explanatory valuethan hazard exposure. This suggests two interesting analyses forfuture research. First, whether including more detailed informa-tion in a local index replicates this. And second whether such astraight-forward index, run multiple times with a diverse range ofclimate model projections, would make it possible to quantify theimportance of the hazard and changes in hazard, relative toadaptation measures, which so far seem to be more important, andtherefore quantify the specific climate change threat, within anuncertainty range.

To date, most assessment of climate impacts on food securityhave focused on changes in production without incorporatingdetailed socioeconomic analyses. This index presents an attempt tocontribute to more comprehensive climate change and foodsecurity assessments, as well as the debate on vulnerability.Replication of the index at smaller scales may inform planning andguide programmes with food security and climate changeadaptation outcomes.

Acknowledgements

The authors would like to express their gratitude to RogerioBonifacio, Carlo Scaramella, Niels Balzer, Oscar Ekdahl, CatherineZanev, and two anonymous reviewers for insightful comments onearlier drafts of this manuscript.

ect impacts of climate related disasters. Mortality from climate-related disasters

ive exposure of a country’s population: the higher the proportional death toll from

e country’s vulnerability to adverse changes in climate (World Bank and United

important impact of climate-related disasters. Because absolute damage is larger

ore property), the losses should be scaled by GDP to allow for meaningful

tive impact of disasters on economies (World Bank and United Nations, 2010).

mic losses also imply lower ability to manage climate risks.

sulting from insufficient rainfall) are expected to increase in duration, frequency

ult in agricultural losses and are a major driver of food insecurity (Burke et al.,

wn for the major crops (barley, maize, rice, sorghum, soya bean and wheat) have

centage of area affected by drought since the 1960s, from approximately 5-10

-25 percent (Li et al., 2009). 700 million people suffering from food insecurity are

and this number is expected to increase due to climate change and population

CCD, 2010).

ed by too much rainfall, leading to flood events. The impact of climate change on

Globally, total rain falling during heavy rainfall events is increasing, and models

ore heavy rainfall events as the climate warms (Held and Soden, 2006). Floods can

de areas, as well as devastating food stores, assets (such as farming equipment) and

ation) (Falloon and Betts, 2010).




Appendix A (Continued )

Component Indicator Rationale

Number of storms Tropical cyclones are an additional extreme weather event that might impact on food security and nutrition.

Some studies (e.g. Gray, 1990) suggest that, globally, tropical cyclones may become more intense in the future

with stronger winds and heavier precipitation but regional variations in cyclogenesis are less well understood.

High resolution models also indicate the possibility of a decrease in future global tropical cyclone frequency,

but with increased intensity and duration (MacDonald et al., 2005; Bengtsson et al., 2007; Gualdi et al., 2008).

Cyclones also have the potential to devastate a region, causing loss of life and widespread destruction to

agricultural crops, land, infrastructure, and livelihoods (Meehl et al., 2007).

Sensitivity Forest cover Forest degradation affects food security in several complex ways. Forests directly or indirectly supply food to

over 300 million people annually (Pimentel et al., 1997). Forests help to protect land and water resources,

maintaining the productivity of agricultural and environmental systems (Lipper, 2000). Forests can also

provide protection against climate-induced disturbances such as droughts, floods, and storms, directly through

physical barriers, and indirectly through prevention of land degradation (Dale et al., 2001).

Rainfed agriculture Rainfed agriculture is sensitive to the impacts of climate change as it depends on predictable rainfall patterns

(Wani et al., 2009). Increasingly erratic weather patterns will render rainfed agriculture highly vulnerable

under climate change. This is especially problematic where high reliance on rainfed agriculture is combined

with increasingly erratic rainfall. These conditions coincide in sub-Saharan Africa, where 95 percent of the

agricultural land is rainfed, and where rainfall is expected to become more erratic (IPCC, 2007).

Cereal yield (crop production) Crop production (cereal yield, kg/ha) is an important component of food security, and is likely to be affected

under climate change. Direct impacts include changes in agro-ecological conditions; indirect impacts include

changes in economic growth and distribution of incomes, which in turn affect demand for agricultural produce

(Schmidhuber and Tubiello, 2007). The quality and quantity of cropland available might decrease under

climate change: in sub-Saharan Africa especially, land for double cropping could decline by between 10 and 20

million hectares and land suitable for triple cropping could declines by 5–10 million hectares due to changes in

agroecological conditions and precipitation (Fischer et al., 2002).

Adaptive capacity Water access (rural) Water access in rural areas is important for food security, especially in the context of irrigation. Irrigated

agricultural land comprises less than one-fifth of all area but accounts for 40–45% of the world’s food (Doll and

Siebert, 2002). At the same time, the patterns and timings of precipitation are expected to change. In sub-

Saharan Africa this could result in increased drought incidence (IPCC, 2007). Ensuring access to water will be an

important component of food security interventions.

Water access (urban) Ensuring access to water is also important for health and sanitation (UNSCN, 2010). Changes in water quality

and availability, particularly in urban settings with increasingly limited water resources, will have an impact

on sanitation systems. Lack of adequate sanitation exacerbates problems of diarrhoeal infection and food-

borne disease (Confalonieri et al., 2007; IPCC, 2007).

Paved roads Access to roads is an important component of food security. Roads increase agricultural production by

intensifying land use and consolidating the links between agricultural and non-agricultural activities, and

between rural and urban areas (Rosegrant and Cline, 2003; also IFAD, 1995). Expenditure in roads can also lead

to new employment opportunities and livelihood diversification (Fan et al., 2000). Roads can also provide

means of evacuation during climate-related disasters.

Governance Governance structures and good institutions are essential for effective risk management practices (World Bank

and United Nations, 2010). The quality of governance also matters in the distribution of resources to manage

hunger and climate-related risks; further, good governance structures boost the confidence of donors to invest

in a country (ISDR, 2009).

Population growth rate Rapid population growth has implications for food security as there will be growing demand for food products,

which will have to be met by increasing agricultural production and intensification Leisinger et al. (2002).

Population below poverty line Poverty limits the ability of individuals to purchase food, and exacerbates food insecurity (Maxwell and

Frankenberger, 2007). Poverty also reduces the ability of populations to withstand the adverse impacts of

climate change (Wisner et al., 2004). Adverse climate impacts coupled with high poverty incidence could

increase vulnerability.

Vulnerable employment Vulnerable employment consists of own-account workers and contributing family workers who lack formal

work arrangements, access to benefits or social protection programmes (mostly in rural settings) (ILO, 2009).

These populations are more ‘‘at risk’’ to economic fluctuations arising from climate change. The share of

vulnerable employment therefore is a proxy of social protection needs (FAO, 2010): a high proportion of

vulnerable employment also indicates greater vulnerability to climate change.

Rural population There are strong links between rural populations and food insecurity. Almost three-quarters of the world’s

poor live in rural areas and depend on agriculture for their livelihood. The higher the proportion of population

that obtains its income solely from subsistence farming, the higher the incidence of undernourishment—this

can be largely attributed to lower ability to purchase good quality food (Von Braun et al., 2004).

Appendix B. Table of tested indicators (the indicators used for the index are shown in bold)

Category Indicator Source Correlation to

undernourishment

(Pearson’s r)

p-value Notes

Proxy for hunger

Hunger Undernourishment(%)

FAOSTAT NA NA Most comprehensive global dataseton hunger

Indicators

Vulnerability Component: Exposure

Exposure (hazards) Mortality frommeteorologicalhazards (per 100,000people)

EM-DAT 0.611 <0.05


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Please cite this article in press as: Krishnamurthy, P.K., et al., A methodological framework for rapidly assessing the impacts of climaterisk on national-level food security through a vulnerability index. Global Environ. Change (2014), http://dx.doi.org/10.1016/j.gloenvcha.2013.11.004



Appendix B (Continued )


undernourishment

(Pearson’s r)

p-value Notes

Exposure (hazards) Number of droughts(1990–2010)

EM-DAT 0.412 <0.05

Exposure (hazards) Number of floods(1990–2010)

EM-DAT 0.191 <0.05

Exposure (hazards) Number of storms(1990–2010)

EM-DAT 0.134 <0.05

Exposure (hazards) Reported economiclosses (% of GDP)

EM-DAT 0.212 <0.05

Exposure (hazards) Mean annual

temperature (national)

WMO 0.089 0.112 At national scales, this information

hides regional climate variations

Vulnerability Component: Sensitivity

Sensitivity (agriculture) Cereal yield (kg/cultivated ha)

WDI (WorldDevelopmentIndicators,World Bank)

�0.611 <0.05

Sensitivity (markets) Consumer prices

(food)

ILO 0.493 0.0711 Need to be careful about assumptions

and correlations between climate and

food prices.

Sensitivity (agriculture) Land under cereal

production (ha)

WDI �0.488 0.076 High autocorrelation with cereal yields

Sensitivity (markets) Net cereal imports as

percentage of

consumption

FOASTAT 0.441 0.13 The p-value is relatively acceptable

Sensitivity (environment) Forest area (% of totalland)


�0.299 0.09

Sensitivity (agriculture) Arable land (ha/

person)

WDI (World

Development

Indicators,

World Bank)

0.244 0.12

Sensitivity (agriculture) Agricultural rainfedland (% of totalagricultural land)

FAOSTAT/WRI 0.143 <0.05

Sensitivity (agriculture) Food production index WDI (World

Development

Indicators,

World Bank)

0.019 0.806 The index is constructed so that it

measures how much more or less food

has been produced relative to the

arbitrary baseline of 2000

Vulnerability Component: Adaptive capacity

Adaptive capacity (infrastructure) Rural populationwith access to watersources (%)


�0.6 <0.05

Adaptive capacity (infrastructure) Urban populationwith access to watersources (%)


�0.411 0.07

Adaptive capacity (infrastructure) Paved roads (%) WDI (WorldDevelopmentIndicators,World Bank)

�0.422 0.08

Adaptive capacity (governance) Governmenteffectivenessa

WGI (WorldGovernanceIndicators,World Bank)

�0.711 <0.05 Other governance indicators are:voice and accountability, politicalstability, regulatory quality, rule oflaw, control of corruption

Adaptive capacity (governance) Gini coefficientb CIA �0.7 0.09 Autocorrelated with other indicators

Adaptive capacity (governance) GDP per capita WDI (World

Development

Indicators,

World Bank)

�0.599 <0.05 Autocorrelated with population Below

Poverty Line

Adaptive capacity (governance) Social insurance

coverage (number)

ILO �0.511 0.21 Not available in percentages, so difficult

to compare

Adaptive capacity (governance) Human settlements:

lack of durable housing

(units)

WRI 0.497 0.19 Incomplete dataset but can test for key

countries

Adaptive capacity (governance) Social expenditure ($/

capita?)

OECD �0.476 0.25 Incomplete dataset

Adaptive capacity (governance) Dependency ratio

(units)

ILO 0.412 0.09

Adaptive capacity (governance) Social benefits from

government (units)

UNSD �0.114 0.23


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Please cite this article in press as: Krishnamurthy, P.K., et al., A methodological framework for rapidly assessing the impacts of climaterisk on national-level food security through a vulnerability index. Global Environ. Change (2014), http://dx.doi.org/10.1016/j.gloenvcha.2013.11.004



Appendix B (Continued )


undernourishment

(Pearson’s r)

p-value Notes

Adaptive capacity (governance) Literacy rate (% of adult

population)

WDI (World

Development

Indicators,

World Bank)

0.309 0.08 Slightt autocorrelation with

government effectiveness

Adaptive capacity (governance) Life expectancy at

birth (years)

UNDP �0.184 0.08

Adaptive capacity (governance) Gross savings (% of

GDP)

WDI (World

Development

Indicators,

World Bank)

�0.149 <0.05 Slight autocorrelation with

government effectiveness

Adaptive capacity (governance) GDP growth (reported

annual %)

WDI (World

Development

Indicators,

World Bank)

�0.145 0.189 The low correlation is due to the large

anomalies and the large changes in GDP

in recent years. The trend also indicates

that GDP growth is associated with

higher undernourishment because

there is higher GDP growth in

undernourished countries.

Adaptive capacity (governance) National savings (% of

national gross income)

WRI �0.128 0.08

Adaptive capacity (demographics) Poverty gap at $2 aday (defined aspoverty line) (PPP) (%of total pop)


0.799 <0.05

Adaptive capacity (demographics) Poverty gap at national

poverty line (%)


0.661 0.3 Incomplete dataset

Adaptive capacity (demographics) Rural population (%of total)


0.621 <0.05

Adaptive capacity (demographics) Poverty gap at rural

poverty line (%)

WDI (World

Development

Indicators,

World Bank)

0.596 <0.05

Adaptive capacity (demographics) Poverty gap at $1.25 a

day (PPP) (%)

WDI (World

Development

Indicators,

World Bank)

0.576 0.221 Incomplete dataset

Adaptive capacity (demographics) Vulnerableemployment (% oftotal labour force)


0.523 0.07

Adaptive capacity (demographics) Decadal populationgrowth

UNPD 0.511 <0.05

Adaptive capacity (demographics) Unemployment (%

total labour force)

WDI (World

Development

Indicators,

World Bank)

0.488 0.09 Autocorrelated with population below

poverty line

Adaptive capacity (demographics) Poverty headcount

ratio at rural poverty

line (% of rural pop)

WDI (World

Development

Indicators,

World Bank)

0.312 <0.05

Adaptive capacity (demographics) Slum population (% of

total population)

WRI 0.293 0.121 Autocorrelation with poverty

indicators

Adaptive capacity (demographics) Lack of sufficient living

area (units)

WRI 0.121 0.421 Slight autocorrelation with other

indicators but dataset is incomplete

a Government effectiveness measures public perceptions of the quality of public services, the quality of the civil service and the degree of independence from political

pressure, the quality of legislation and the credibility of the government’s commitment to these laws. See World Bank (2010) Worldwide Governance Indicators. Available at:

http://info.worldbank.org/governance/wgi/mc_countries.asp.b World Bank (2011). Poverty Analysis: Measuring Inequality. Available at: http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTPOVERTY/EXTPA/0,con-

tentMDK:20238991�menuPK:492138�pagePK:148956�piPK:216618�theSitePK:430367,00.html.


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A methodological framework for rapidly assessing the impacts of climate risk on national-level food...

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