Water Issues Questionnaire Analysis

1. WP 02 Water issues questionnaire analysis Barbara Del Corpo, Ugo Gasparino Management Board Meeting, Casablanca 11-12 | 11 | 05

2. Complementary approaches

3. Water issues questionnaire - the objective • To develop a reliable, consistent, and shared information basis for the policy and decision making process in water management, through an interactive participatory approach • To discover, in each case study, the most critically perceived issues • To identify (dis)similarities among different watersheds • To identify different perceptions of the same issues by different stakeholders (in the same case study): • comparison in a horizontal and vertical sense (at the same/different geographical level: national/national, local/local, national/local) • among actors of the same/different scope (private/public, research/non research)

4. the basis • The questionnaire is based upon an exhaustive literature review about: • climate change • water pollution and scarcity • water management and water policies • sanitation and gender • water pricing • PP

5. the basis/2 • The questionnaire: • reflects problems on a River Basin scale • reflects the demand/supply “Water Budget” scheme • reflects the DPSIR framework • is structured into three main components: • Water management • Water demand • Water supply • addresses both quantitative and qualitative (static and dynamic) issues

6. codification • Each issue is scored on a simmetricseven-point semantic ordinal scale ranging from “extremely unimportant” to “extremely important” (plus don’t know, used whenever one believes not to have sufficient information to rank the issues). Extremely Very Unimportant Neutral Important Very Extremely unimportant unimportant important important Don’t know

7. the questionnaire

8. “physical conditions” section • This section addresses the basic physical characteristics of the case study area, inasmuch as they contribute to the subsequent specific water resources management-related issues and problems • Water scarcity • Floods • Droughts • Groundwater availability • Watershed degradation • Coastal interaction

9. “water management” section • This part deals with the existence of clearly established competences of water institutions, efficient mechanisms of water regulation (standards, quotas, water rights), socially and economically acceptable forms of allocation and tariff structures, effective stakeholder participation (including gender equity), adequate managementresources for investments in infrastructures maintenance, technologies and research • Institutional framework • Regulatory framework • Water pricing policies • Education and awareness • Gender issues • Technology and investments

10. “water demand” section • This section deals with the economic sectors and spheres of human activity which, through their demand of water resources, exert a pressure on the resource causing scarcity problems (both quantity and quality) • Households • Tourism • Agriculture • Industry • Water quantity • Water quality • Water-saving technologies • Increasing stress on water resources expected to be driven by population growth/tourism growth/agriculture growth/industrialization

11. “Water demand” section/2 “water supply” section • This section assesses the state of water resources (internal surface water and groundwater, recyclable annd recycled water resources, water imports) in terms of quantity and quality and some of the impacts on the surface and groundwater compartments (water degradation). • The section also considers the limits to economic development and to the quality of life of the population due to the same conditions of water scarcity, requiring new solutions (e.g., land use change, or low consumption technologies) for a better management of such scarce resources, also preserving the ecological status of aquatic ecosystems. • Quantity • Quality • Infrastructures

12. OPTIMA application Extremely Very Unimportant Neutral Important Very Extremely unimportant unimportant important important Don’t know • Collected data: • scorings assigned to 64 Issues of the Questionnaire by 75 Stakeholders (seven case studies). • Symmetric 7-point ordinal scale ordered categories: • from “extremely unimportant” to “extremely important” (+ “missing values”) rank ordering of observations rather than precise measurements.

13. Statistical analysis • univariate analysis - the responses to each individual Issue of the Questionnaire analysed as independent data sets  frequency distributions, descriptive statistics, average rank, “across groups” comparison… • bivariate analysis - correlations between couples of different Items  64 variables > 2000 independent correlations! • multivariate analysis - untangle the overlapping information provided by the correlated variables peer beneath the surface to check the existence and consequently “discover” any “underlying structure” simplify the original data set by seeking to express what is going on in terms of a reduced set of new dimensions (whose meaning can be possibly interpreted).

14. Preprocessing • Ordinal scale - rank ordering of observations rather than precise measurements two alternative approaches: • “more consistent treatment” (polychoric correlation,…) • straightforward replacement of ordinal categories with integer scores (e.g., 1,2,…,7) and applying traditional statistical methods as if the variables were continuous. • Missing Values - imputed by the regression method.

15. Global view • The respondents tend prevalently to select the “important”, “very important” and “extremely important” scores. Fewer people answered on the “unimportant” branch of the scale.  6%

16. Univariate Analysis Ranking . . .

17. Bivariate Analysis Correlation Matrix v1 v2 v3 v4 v5 v1 v2 v3 v4 v5

18. Multivariate Factor Analysis Factor Analysis “Re-organization” of the Correlation matrix v1 v2 v3 v4 v5 v1 v2 v3 v4 v5

19. Aims of the Analysis • Discovery and interpretation of “latent variables” • main aim: untangle the overlapping information provided by correlated variables  “discover” an “underlying structure” (“latent variables”). • latent variables interpreted as underlying fundamental quantities from which the original variables take origin  unobserved variables that determine the patterns of observations (“cognitive map”) • a small number of latent variables (whose meaning can be possibly interpreted) might explain a large number of measurements  simplification of the set of the original data • the unobserved latent variables can be much more interesting than the original set of observations  they can concisely make clear the behaviour of the observations • by modelling the relevant collected information as having origin from these limited number of latent variables better understanding of the phenomenon and reduction of the dimensionality of the problem

20. Number of Factors • How many factors to extract??? • too few (underfactoring)  tends to “telescope” factors together the “right” number! • too many (overfactoring)  tends to extract “bloated specific” factors

21. Perceived Issues 4 independent (orthogonal ) factors • 1st factor: “Pressure” and “impact” on water demand and quality, mainly related to non-agricultural “driving forces” (tourism, household, industry) • Includes growths of “driving forces” and unsatisfactory infrastructure • 2nd factor: Deficiencies in the regulatory and institutional “response” (DPSIR Framework), mainly in relation with Agriculture • Includes deficiencies in the infrastructure, conflicts • 3rd factor: Techno-economical barriers and (industrial) impact on water quality (limiting its further use due to “too low” quality) • Includes obsolete technologies, maintenance and techno-economical barriers • 4th factor: “Subventioned” water price (agriculture and household) • “too low” water price, with respect to the implementation of a “full cost recovery”

22. within- vs. between-variance JOR PAL TUN MOR LEB TUR CYP less critical more critical (non-agricultural) “pressure” and “impact” on water demand and quality

23. within- vs. between-variance JOR PAL TUN MOR LEB TUR CYP less critical more critical Deficiencies in regulatory and institutional “response” (agriculture)

24. within- vs. between-variance JOR PAL TUN MOR LEB TUR CYP less critical more critical Techno-economical barriers and (industrial) impact on water quality Menemem Left Bank Irrigation Association Menemem Right Bank Irrigation Association

25. within- vs. between-variance JOR PAL TUN MOR LEB TUR CYP less critical more critical “Subventioned” water price (agriculture and household) Local Communities

26. classification - discrimination Deficiencies in the regulatory and institutional “response” (non-agricultural) “pressure” and “impact” on water demand and quality

27. (dis)similarities

28. Stakeholders’ priorities Corridoio Zero: 4 classifications of Stakeholders Stakeholder “scale” less critical more critical (non-agricultural) “pressure” and “impact” on water demand and quality Similar for the 4th factor: “Subventioned” water price (agriculture and household): “local” tend to assign lower scorings

29. Summary and Conclusions • Watershed Management is a “complex” task • In a “participative approach”, response policies should take into account the priorities perceived by different Stakeholders • Questionnaires (“social sciences” approach) are a powerful tool to investigate how criticalities/ risks/priorities are perceived • a “Water Issue Questionnaires” has been developed, tested and applied in different case studies • 4 “latent factors” “tend to emerge” from the statistical analysis • The 4 “factors” allow a classification/discrimination among the different Case Studies/Stakeholders

30. Future work • The present form of the Questionnaire tends to be somewhat “redundant”: several Items seem to be perceived as “paraphrases of the same questions” by the “average respondent”. This effect could be attenuated by eliminating few of the redundant Issues or (as a better choice) by creating “sum scales” before the data are analysed. • As a consequence of the relatively small sample size of the available data (especially if compared to the number of variables), the generality of the obtained results cannot be “guaranteed” (i.e., they could partially be “data specific artefacts” and not emerge in other “equivalent samples”). • A validation of the results on an independent dataset (or a further increase in the number of compiled questionnaires) is therefore highly desirable.

31. Redundancy Tourism’s related

32. Redundancy Agriculture’s related

33. Best Greetings!

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