Climate Change Vulnerability. Padjadjaran University/EEPSEA

Transcription

1 Constructing The Index of Climate Change Vulnerability Arief Anshory Yusuf Arief Anshory Yusuf Padjadjaran University/EEPSEA

2 Reference

3 1 frameork (Ideal) Steps in constructing composite indicators: Overvie S teps in co onstructin 1. Theoretical frameork Normalisation. Robustness and. Back to the real.. Presentation and

4 1 frameork Provides the basis for the selection and combination of into a meaningful composite indicator S teps in co onstructin Defining the concept Get a clear sense of hat is being measured Climate Change Vulnerability: The degree to hich a system is susceptible to, or unable to cope ith the adverse effects of climate change, including climate variability and extremes. Determining dimensions into sub-groups of indicators Divide multi-dimensional concepts into several sub-groups. Think in terms of nested structure. Involve experts and stake holders as much as possible Identifying the selection criteria Should an indicator be included? Think in term of a function ==> Output = F(Input)

5 S teps in co onstructin 1 Theoretical frameork Climate Change Vulnerability (of region i) V i = F(H i, S i, A i ) Climatic Hazards (H) Vulnerability to Climate Change (V)

6 1 frameork Sub-grouping and nested structure Typhoon S teps in co onstructin Climate Change Vulnerability Hazards Exposure Sensitivity Adaptive capacity Flood Drought Landslide Sea level rise Human Ecosystem Socio-economics Technology Well-being Poverty Inequality Electricity Irrigation Road Infrastructure Communication

7 1 Theoretical frameork Climate Change Vulnerability: Issues on conceptuality and selection S teps in co onstructin Climate change is a future problem ==> future vulnerability Future hazards Future Future adaptive capacity There are prediction of climatic (temp, rainfall), but.. Are there prediction on the hazards materialized from them? Ho uncertain? Are they available AND accessible? Compromise beteen completeness and poor input Assumption: Geographically, the big picture ill not change much ithin 5-50 years.

8 1 frameork To provide a complete set S teps in co onstructin Implicit Modeling Hot deck imputation. Filling in blanks cells ith individual, dran from similar responding units. For example, missing values for individual income may be replaced ith the income of another respondent ith similar characteristics, e.g. age, sex, race, place of residence, family relationships, job, etc. Substitution. Replacing non-responding units ith unselected units in the sample. For example, if a household cannot be contacted, then a previously non-selected household in the same housing block is selected. Cold deck imputation. Replacing the missing value ith a value from an external source, e.g. from a previous realisation of the same survey.. Explicit Modeling Unconditional mean/median/mode imputation. The sample mean (median, mode) of the recorded values for the given individual indicator replaces the missing values. Regression imputation. Missing values are substituted by the predicted values obtained from regression. The dependent variable of the regression is the individual indicator hosting the missing value, and the regressor(s) is (are) the individual indicator(s), shoing a strong relationship ith the dependent variable, i.e. usually a high degree of correlation. Example from EEPSEA SEA CC Vulnerability study Combining past provincial GDP ith current national GDP. Using consumption expenditure instead of GDP.

9 1 frameork Objectives: to study the overall structure of the set, assess its suitability, and guide subsequent methodological choices (e.g., eighting, ). S teps in co onstructin To check the underlying structure of the along the to main dimensions, namely individual indicators and countries/regions (by means of suitable multivariate methods, e.g., principal components, cluster ). To identify groups of indicators or groups of countries/regions that are statistically similar and provide an interpretation t ti of fth the results. To compare the statistically- determined structure of the set to the theoretical frameork and discuss possible differences.

10 1 frameork Types of normalization S teps in co onstructin Others: Indicators above or belo the mean Cyclical indicators (OECD) Balance of opinions (EC) Percentage of annual differences over consecutive years

11 1 frameork Types of normalization S teps in co onstructin Ranking Simple Not affected by outliers Regions can follo their relative position Z-score Zero mean and 1-SD Extreme value have effect on indicator Min-Max [0,1] Outliers can distort Widen the range of small intervals

12 1 frameork Weighting Methods S teps in co onstructin Equal eights Based on statistical models Principal components Data envelopment Regression Unobserved components models Based on public/expert opinions i Budget allocation Public opinion Analytic Hierarchy Process Developed dby Thomas Saaty in 10s. Conjoint

13 1 frameork Analytic Hierarchy Process S teps in co onstructin AHP is used to assign Climate Change Vulnerability Hazards Exposure Sensitivity Adaptive capacity Typhoon Flood Drought Landslide Sea level rise Human Ecosystem Socio-economics Technology Infrastructure Well-being Poverty Inequality Electricity Irrigation Road Communication

14 1 frameork Analytic Hierarchy Process: Example AHP is used to assign Typhoon Hazards Exposure Flood Drought Landslide Sea level rise Human S teps in co onstructin Climate Change Vulnerability Suppose e ant to assign eight to the three determinants of adaptive capacity Sensitivity Adaptive capacity Ecosystem Socio-economics Technology Well-being Poverty Inequality Electricity Irrigation Road Infrastructure Communication

15 1 frameork Analytic Hierarchy Process: Example S teps in co onstructin () Invite some representative experts and stake holders and Ask each of them the folloing questions: Which one do you think is more important in determining adaptive capacity? SOCIO-ECONOMICS INFRASTRUCTURE Very Very Extreme strong Strongly Slightly Slightly Strongly strong Extreme Equal SOCIO-ECONOMICS Extreme Very strong Strongly Slightly Equal Slightly The scale is typically 1- Strongly TECHNOLOGY Very strong Extreme INFRASTRUCTURE Very Very Extreme strong Strongly Slightly Equal Slightly Strongly TECHNOLOGY strong Extreme 5 1 5

16 1 frameork Analytic Hierarchy Process: Example S teps in co onstructin SOCIO-ECONOMICS (S) INFRASTRUCTURE (I) Very Very Extreme strong Strongly Slightly Slightly Strongly Equal strong Extreme SOCIOECONOMICS(S) Extreme Extreme Very strong Strongly Slightly Equal Slightly Strongly TECHNOLOGY(T) Very strong Extreme INFRASTRUCTURE (I) Very strong Strongly gy Slightly Slightly Strongly gy Equal TECHNOLOGY (T) Very strong Extreme S > I; I > T => S > T <= this expert is quite consistent (transitivity), But it is not alays the case.

17 1 frameork Analytic Hierarchy Process S teps in co onstructin Infrastructure Technology Socio- economics Socioeconomics 1 5 Infrastructure 1/5 1 =A Technology 1/ 1/ 1

18 1 frameork Analytic Hierarchy Process S teps in co onstructin A = 1/5 1 x = 0.1 1/ 1/ 1 x is the normalized eigenvector of A Adaptive capacity AHP also calculate the consistency ratio or CR. If CR < %, the judgment is consistent We can average over experts to find final eights. Socio-economics Technology Infrastructure

19 1 frameork Analytic Hierarchy Process S teps in co onstructin AHP is used to assign Climate Change Vulnerability Hazards Exposure Sensitivity Adaptive capacity Then do the same across the hyrarchies. Typhoon Flood Drought Landslide Sea level rise Human Ecosystem Socio-economics Technology Infrastructure Well-being Poverty Inequality Electricity Irrigation Road Communication

20 1 frameork Aggregation S teps in co onstructin Additive Preference independence Separate marginal contribution No synergy nor conflict Full compensability Geometric Preference dependence Marginal contribution ti is not separable No full compensability

21 1 frameork Aggregation: Additive versus Geometric S teps in co onstructin Country A B Indicator 1 1 Indicator 1 Indicator 1 Indicator 1 Additive Geometric.1.00 Country A B Indicator 1 1 Indicator Indicator 1 Indicator 1 Additive.5.5 Geometric.55. Additive.1%.1% Geometric 1.%.%

22 1 Theoretical frameork : More formally, is the study of ho the variation in the output can be apportioned, qualitatively or quantitatively, to different sources of variation in the assumptions, and of ho the given composite indicator depends upon the information fed into it. The mechanism for including or excluding an indicator, the normalization scheme, the imputation of missing, the choice of eights, the method. teps in co onstructin S To consider a multi-modeling approach to build the composite indicator, and if available, alternative conceptual scenarios for the selection of the underlying indicators. To identify all possible sources of uncertainty in the development of the composite indicator and accompany the composite scores and ranks ith uncertainty bounds. To conduct of the inference (assumptions) and determine hat sources of uncertainty are more influential in the scores and/or ranks.

23 1 frameork S teps in co onstructin To reveal the main drivers for an overall good or bad performance. Transparency is primordial to good and policymaking. To profile country performance at the indicator level so as to reveal hat is driving the composite indicator results. To check for correlation and causality (if possible). To identify if the composite indicator results are overly dominated by fe indicators and to explain the relative importance of the sub-components of the composite indicator. Statistically: Path Analysis, Structural Equation Modeling (SEM), Bayesian netork.

24 1 frameork S teps in co onstructin Should be made to correlate the composite indicator (or its dimensions) i ith existing (simple or composite) indicators as ell as to identify linkages through regressions. To correlate the composite indicator ith other relevant measures, taking into considerationtheresultsof. To develop -driven narratives based on the results.

25 1 frameork S teps in co onstructin Should receive proper attention, given that the visualisation can influence (or help to enhance) interpretability. To identify a coherent set of presentational tools for the targeted audience. To select the visualisation technique hich communicates the most information. To present the composite indicator results in a clear and accurate manner. MAPPING! A picture is orth a thousand ords, a map is orth a million ords.