University Of Notre Dame Global Adaptation Index
University of Notre Dame Global Adaptation IndexCountry Index Technical ReportChen, C.; Noble, I.; Hellmann, J.; Coffee, J.; Murillo, M.; Chawla, N.Release date: November, 2015TABLE OF CONTENTSContributing Experts . 1I. Introduction . 2II. ND-GAIN Country Index Overview . 3Terminology . 3Selecting ND-GAIN indicators . 4Calculating The ND-GAIN Score . 6THE ND-GAIN Matrix. 9III. ND-GAIN indicators . 10IV. ND-GAIN measure description, rationale, calculation, data sources . 11Food .12Water .15Health .19Ecosystem Services.22Human habitat .26Infrastructure .29Economic readiness.33Governance readiness .33Social readiness.35V. ND-GAIN Reference Points. 38VI. Works Cited . 40Contributing ExpertsCountry ND-GAIN Index Contributing Experts:Clark, Michael Statistical consultant, Center for Statistical Consultation and Research, University ofMichiganBlock, Emily Associate Professor at University of Alberta Business SchoolGassert, Francis Lead, Data for Impact, World Resource Institute1
Gonzalez, Patrick Climate Change Scientist, U.S. National Park ServiceJishan, Liao Research Assistant, University of Notre DameLodge, David Professor, Department of Biological Science, University of Notre DameMichael, Edwin Professor, Department of Biological Science, University of Notre DameMartinez, Andres Independent ConsultantMayala, Benjamin PhD candidate, University of Notre DameMurphy, Patrick Director of Public Sector Engagement, Palo Alto Research CenterMusumba, Mark Associate Research Scientist, Earth Institute, Columbia UniversityRegan, Patrick Professor, Department of Political Science, Kroc Institute for International PeaceStudies, University of Notre DameShiao, Tien Sustainability Relations, H&MWozniak, Abigail Associate Professor, Department of Economics, University of Notre DameI. INTRODUCTIONThe Notre Dame-Global Adaptation Index (ND-GAIN) Country Index is a free opensource index that shows a country’s current vulnerability to climate disruptions. It alsoassesses a country’s readiness to leverage private and public sector investment foradaptive actions. ND-GAIN brings together over 74 variables to form 45 core indicatorsto measure vulnerability and readiness of 192 UN countries from 1995 to the present(Due to data availability, ND-GAIN measures vulnerability of 182 countries andreadiness of 184 countries)Corporate, NGO, government, and development decision-makers use ND-GAIN’scountry-level rankings and underlying data to make informed strategic operational andreputational decisions regarding supply chains, capital projects, policy changes andcommunity engagements.Notre Dame Global Adaptation Index moved to the University of Notre Dame in April2013. It was formerly housed in the Global Adaptation Institute in Washington, D.C. Itnow resides within the Climate Change Adaptation Program of the University of NotreDame’s Environmental Change Initiative (ND-ECI), a Strategic Research Initiativefocused on “science serving society” and draws on resources from both inside andoutside of the university.Adaptation is an evolving concept. Our understanding of climate change and the risks itpresents is constantly improving through ongoing research. At ND-GAIN, we strive toestimate adaptation risk and opportunity using the best available research outputs,data, and tools. To this end, the index keeps updating whenever it is necessary, andhighlights of each release can be found at http://index.gain.org/about/reference. As wereceive feedback from our users, we also periodically release new tools for datavisualization and analytics.2
This report describes ND-GAIN for its November 2015 release and provides detailedinformation on the framework, data sources, and data compilation process used forproducing the Index.II. ND-GAIN COUNTRY INDEX OVERVIEWAll countries, to different extents, are facing the challenges of adaptation. Due togeographical location or socio-economic condition, some countries are morevulnerable to the impacts of climate change than others. Further, some countries aremore ready to take on adaptation actions by leveraging public and private sectorinvestments, through government action, community awareness, and the ability tofacilitate private sector responses. ND-GAIN measures both of these dimensions:vulnerability and readiness.TERMINOLOGYND-GAIN’s framework breaks the measure of vulnerability into exposure, sensitivityand adaptive capacity, and the measure of readiness into economic, governance andsocial components. The construction of the ND-GAIN framework is based on publishedpeer-reviewed material, the IPCC Review process, and feedback from corporatestakeholders, practitioners and development users. Most of the vulnerability andreadiness measures (except indicators of exposure – see below) are said to beactionable, meaning that these represent actions or the result of actions taken bynational governments, communities, Civil Society Organizations, Non-GovernmentOrganizations, and other stakeholders.VulnerabilityPropensity or predisposition of human societies to be negatively impacted by climatehazardsND-GAIN assesses the vulnerability of a country by considering six life-supportingsectors: food, water, health, ecosystem services, human habitat and infrastructure.Each sector is in turn represented by six indicators that represent three cross-cuttingcomponents: the exposure of the sector to climate-related or climate-exacerbatedhazards; the sensitivity of that sector to the impacts of the hazard and the adaptivecapacity of the sector to cope or adapt to these impacts.Exposure: The extent to which human society and its supporting sectors are stressedby the future changing climate conditions. Exposure in ND-GAIN captures the physicalfactors external to the system that contribute to vulnerability.Sensitivity: The degree to which people and the sectors they depend upon are affectedby climate related perturbations. The factors increasing sensitivity include the degree3
of dependency on sectors that are climate-sensitive and proportion of populationssensitive to climate hazard due to factors such as topography and demography.Adaptive capacity: The ability of society and its supporting sectors to adjust to reducepotential damage and to respond to the negative consequences of climate events. InND-GAIN adaptive capacity indicators seek to capture a collection of means, readilydeployable to deal with sector-specific climate change impacts.ReadinessReadiness to make effective use of investments for adaptation actions thanks to a safeand efficient business environmentND-GAIN measures readiness by considering a country’s ability to leverageinvestments to adaptation actions. ND-GAIN measures overall readiness by consideringthree components: economic readiness, governance readiness and social readiness.Economic Readiness: The investment climate that facilitates mobilizing capitals fromprivate sector.Governance Readiness: The stability of the society and institutional arrangements thatcontribute to the investment risks. A stable country with high governance capacityreassures investors that the invested capitals could grow under the help of responsivepublic services and without significant interruption.Social readiness: Social conditions that help society to make efficient and equitable useof investment and yield more benefit from the investmentSELECTING ND-GAIN INDICATORSTo identify indicators that reflect climate vulnerability and adaptation readiness, theND-GAIN team surveyed the most recent literature and consulted scholars, adaptationpractitioners, and global development experts. The indicators included in ND-GAINwere chosen to fit within the structure described above and to meet the followingcriteria: Focus on sectors and components that have impacts on human well-being,including biophysical impacts of climate change on a country's society, and thesocioeconomic factors that either amplify or reduce such impacts. Indicators that represent vulnerability or readiness should be actionable forclimate change adaptation. In other words, governments and private sector orcommunities could take actions on an issue and expect to see changes in one ormore indicators over time. Exceptions are the exposure indicators, which are not4
actionable through adaptation, as they are mostly driven by biophysical factors andare only actionable through greenhouse gas abatement (climate change mitigation). Representatives of vulnerability sectors or readiness components, based onrelevant literature and climate change adaptation practices (i.e. the adaptationactions taken by individuals or the adaptation programs run by countrygovernments, bilateral or multilateral aid agencies, international organizations,NGOs, private investors and so forth). When possible, indicators should have the potential to be scaled down fromcountry to sub-country level, to support the possibility of assessing climatevulnerability and adaptation readiness at finer scales. Two kinds of indicators are explicitly excluded from ND-GAIN. The first is GrossDomestic Product (GDP) per capita or any of its closely related measures. GDP percapita is commonly used in indices relating to development and poverty (e.g.,UNDP's Human Development Index), but including it in ND-GAIN would doublypenalize many developing countries. It is well known that less developed countriesalso have low adaptive capacity and readiness, and high sensitivity. ND-GAIN doesshow a high correlation with a county’s economic status; and a version of ND-GAINthat adjusts the index score using GDP per capita. Second, ND-GAIN does notinclude data on the impact of recent climate-related disasters. Instead, disasterdata provide an independent source of information for decision-making and alsofor possible index validation. The data selected that quantifies the ND-GAIN indicators have the followingfeatures to ensure transparency, reliability and consistency:o Available for a high proportion of United Nations countries.o Time-series so that changes and trends in country vulnerability andreadiness can be tracked. Indicators with data from 1995 to the present arepreferred.o Freely accessible to the public.o Collected and maintained by reliable and authoritative organizations thatcarry out quality checks on their data.o Are transparent and conceptually clear.Figure 1 below summarizes indicators measuring both vulnerability and readiness.5
Figure 1 Summary of ND-GAIN Vulnerability and Readiness IndicatorsVulnerability is composed of 36 indicators. Each component has 12 indicators, crossedwith 6 sectors. Readiness is composed of 9 indicators.CALCULATING THE ND-GAIN SCOREThere are many systematic methods for converting data into an index. For instance:scaling data into similar ranges of values, including normalizing to a common mean andstandard deviation; setting base low and high values for the data (e.g. from theobserved minimum to the observed maximum; or from 0 to 100% compliance etc.), andscaling data either linearly or after transformation to a prescribed range (e.g. 0 to 1; 0to 100; -1 to 1); or converting the data to ranked values.The 45 ND-GAIN indicators come from 74 data sources that provide 74 underlyingdata. 20 of the 45 indicators come directly from the sources; the rest 25 are computedby compiling underlying data. The methods used to compute these 25 indicators aredetailed in Section IV of this report.ND-GAIN follows a transparent procedure for data conversion to index. A detailed,step-wise process is described below and in Figure 2.Step 1. Select and collect data from the sources (called “raw” data), or computeindicators from underlying data. Some data errors (i.e. tabulation errors coming fromthe source) are identified and corrected at this stage. If some form of transformation isneeded (e.g. expressing the measure in appropriate units, log transformation to betterrepresent the real sensitivity of the measure etc.) it happens also at this stage.Step 2. At times some years of data could be missing for one or more countries; sometimes, all years of data are missing for a country. In the first instance, linearinterpolation is adopted to make up for the missing data. In the second instance, theindicator is labeled as "missing" for that particular country, which means the indicator6
will not be considered in the averaging process. However, it is important to have mostof the UN countries present in the data.Step 3. This step can be carried out after of before Step 2 above. Select baselineminimum and maximum values for the raw data. These encompass all or most of theobserved range of values across countries, but in some cases the distribution of theobserved raw data is highly skewed. In this case, ND-GAIN selects the 90-percentilevalue if the distribution is right skewed, or 10-percentile value if the distribution is leftskewed, as the baseline maximum or minimum.Step 1 Select and collect "raw" datafrom 74 sources, correct obviouserrors, and make necessarytransformationStep 4 Define "referencepoint" for each indicatorStep 2 Interpolate missing data, or,if one country has no dataavailable for certain indicators,these indicators are considered"missing" for the country.Step 5 Scale "raw"data to "scores" thathas range from 0 to 1Step 3 Identify baseline minimumand maximum for "raw" data.Step 6 Computevulnerability score andreadiness scoreStep 7 Compute NDGAINFigure 2 Detail Steps to Creating ND-GAINStep 4. Whenever applicable, set proper reference data points for measures. Thereference points stand for the status of perfection, i.e. the best performance thatrepresents either zero vulnerability or full readiness. In some cases reference pointswere the baseline minimum or maximum identified in Step 3. For certain measures,based on the adaptation or development practices, reference points were set bycommon sense. For example, the reference points for child malnutrition is 0%, forreliable drinking water is 100% and so on. If data sources have reference points bydefault for a measure, these are adopted. For instance, the reference point for themeasure “Quality of trade and transport-related infrastructure” is 5, because the rawdata are ranged from 1 to 5 with 5 being the highest score(See reference points sectionbelow).7
Step 5. Scale “raw” data to “score”, ranging from 0 to 1, to facilitate the comparisonamong countries and the comparison to the reference points. Scaling follows theformula below:" " " " "raw" The parameter of “direction” has two values, 0 when calculating score of vulnerabilityindicator; 1 when calculating score of readiness indicators, so that a highervulnerability score means higher vulnerability (“worse”) and a higher readiness scoremeans higher readiness (“better”).Step 6. Compute the score for each sector by taking the arithmetic mean of its 6constituent indicators (all scaled 0-1, weighted equally). Then calculate the overallvulnerability score by taking the arithmetic mean of the 6 sector scores.Step 7. Follow the same process as Step 6 to calculate the overall readiness score.Step 8. Compute the ND-GAIN score by subtracting the vulnerability score fromthe readiness score for each country, and scale the scores to give a value 0 to 100,using the formula below: ! " 1% 508
THE ND-GAIN MATRIXND-GAIN can be represented asa scatter plot of readinessagainst vulnerability, that is,the ND-GAIN Matrix (Figure 3).The Matrix provides a visualtool for quickly comparingcountries and tracking theirprogress through time. Theplot is divided into fourquadrants, delineated by themedian score of vulnerabilityacross all the countries andover all years, and medianscore of readiness calculatedthe same way. Approximatelyhalf the countries fall to the leftFigure 3. The ND-GAIN Matrixof the readiness median andhalf to the right. Similarly, half fall above the vulnerability median and half below1.Red (Upper Left) Quadrant: Countries with a high level of vulnerability to climatechange but a low level of readiness. These countries have both a great need forinvestment to improve readiness and a great urgency for adaptation action.Yellow (Lower Left) Quadrant: Countries with a low level of readiness but also a lowlevel of vulnerability to climate change. Though their vulnerability may be relativelylow, their adaptation may lag due to lower readiness.Blue (Upper Right) Quadrant: Countries with a high level of vulnerability to climatechange and a high level of readiness. In these countries, the need for adaptation is large,but they are ready to respond. The private sector may be more likely participate inadaptation here than in countries with lower readiness.Green (Lower Right) Quadrant: Countries with low level of vulnerability to climatechange and a high level of readiness. These countries still need to adapt (none of themhave a perfect vulnerability score) but may be well positioned to do so.1Note that this does not mean that there will be the same number of countries in each quadrant. Highly ready, oftenwealthy, countries tend to have lower vulnerabilities and vice versa, so proportionately more countries fall in thegreen and red quadrants.9
III. ND-GAIN INDICATORSTable 3 and Table 4 list all the 45 indicators used in the ND-GAIN Index.Table 1 ND-GAIN Vulnerability IndicatorsSectorExposurecomponentProjected change ofcereal y
Notre Dame Global Adaptation Index moved to the University of Notre Dame in April 2013. It was formerly housed in the Global Adaptation Institute in Washington, D.C. It now resides within the Climate Change Adaptation Program of the University of Notre Dame’s Environmental Change Initiative (ND-ECI), a Strategic Research Initiative
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