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WP10:WP10:Progress report Progress report
Programme name: Sustainable Management of Scarce Resources in the Coastal Zone
Program Areas: A3, (d)Project acronym: SMARTContract number: ICA3-CT-2002-10006Project Deliverable:Related Work Package: WP10 Comparative policy analysisType of Deliverable: RE, restrictedDissemination level:Project Partner Fondazione Eni Enrico MatteiDocument Author: Gretel GambarelliEdited by:Reviewed by:Document Version:Revision history:First Availability:Final Due Date:Last Modification: 25.05.2004Hardcopy delivered to:
TABLE OF CONTENTS
WP10: TASKS......................................................................................................................................................4
TASK 1: COMPLETE INDICATORS DATA BASE.......................................................................................5
TASK 2: ORGANIZE IN A DPSIR FRAMEWORK.......................................................................................5
TASK 3: DEFINE SCENARIOS.........................................................................................................................5
TASK 4: LINK SCENARIOS ASSUMPTIONS TO (P) INDICATORS........................................................6
TASK 5: EXTEND INDICATOR LISTS BE MODEL OUTPUTS................................................................7
TASK 6: DEVELOP AGGREGATE INDICATORS (NORMALISED)........................................................7
TASK 7: MC/MO ANALYSIS (NON-DOMINATED ALTERNATIVES BASED ON PREFERENCE STRUCTURES)....................................................................................................................................8
TASK 8: DEFINE A POLICY AND LOGICAL FRAMEWORK CAPABLE TO REPRESENT CASES OF INVESTMENT, RE-ALLOCATION AND RE-STRUCTURING, ETC.................................8
TASK 9: COMPLETE DELIVERABLE...........................................................................................................9
WHAT’S NEXT....................................................................................................................................................9
ANNEX 1: LOGIC FRAME FOR THE DEFINITION OF SCENARIOS AND INDICATORS...............11
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ANNEX 2: CONTRIBUTIONS FROM CASE STUDIES..............................................................................14
EGYPT.................................................................................................................................................................14JORDAN..............................................................................................................................................................16LEBANON...........................................................................................................................................................20TUNISIA..............................................................................................................................................................27TURKEY.............................................................................................................................................................30
ANNEX 3: COMPARATIVE DATA BASE....................................................................................................32
ANNEX 4: DPSIR INDICATORS LIST..........................................................................................................35
ANNEX 5: THE ROLE OF INDICATORS IN SMART................................................................................39
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WP10: TASKS
This list of tasks has been jointly defined during the last internal meeting in Venice (ESS, FEEM, UATLA) and presented during the Aqaba meeting. A score ranging from 1 (initial stage) to 5 (finalized) has been assigned to each task, to indicate the stage of each activity under WP10. The next paragraphs will describe each single task.
TASKS WHO SCORE
1. Complete indicator data base UATLA, FEEM, SUMER, Case Studies Partners
Feedback: ESS, SOGREAH
2. Organise in DPSIR framework (classify) FEEM 3. Define scenarios Case Studies Partners, FEEM
Feedback: ESS, SOGREAH
4. Link scenario assumptions to P indicators by means of models or ES relationships
BECAUSE D indicator in the OPTIMISTIC scenario is valued …
THEREFORE P indicator =…
FEEM, UATLA
Feedback: ESS, SOGREAH
5. Extend indicator lists be MODEL OUTPUTS:
Coastal Water Quality (SOGREAH)
Supply/demand ratios, sectoral distribution, efficiencies
Water and economic efficiencies
ESS, SOGREAH, FEEM
6. Develop derived/aggregate indicators (normalised) or indices
FEEM
Feedback: all partners
7. MC/MO analysis (non-dominated alternatives based on preference structures)
FEEM, ESS, UATLA 8. IF there are clear “winners”, define a policy and logical framework capable to represent cases of investment, re-allocation and re-structuring, efficiencies
FEEM, UATLA, Case Studies Partners (involving stakeholders)
9. Complete Deliverable FEEM, UATLA
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TASK 1: COMPLETE INDICATORS DATA BASE During the last months of 2003, FEEM’s work has been devoted to collect inputs from partners for the definition of scenarios and indicators. The proposed guided exercise (Annex 1: Logic frame for the definition of scenarios and indicators) led the partners to think about the cause-effect relationships that ultimately lead to a certain condition related to the water system, which was perceived to be either acceptable or problematic for each case study. Besides, it was asked to provide estimates for the variables that were perceived to be the main driving forces, with respect to three scenarios (BAU, Optimistic, Pessimistic).
At the beginning of 2004, contributions collected from partners (Annex 2: Contributions from case studies) were analysed and compared by UATLA, which came out with a draft database of relevant indicators, where data for some crucial variables identified for each case study were organized in a comparative data base (Annex 3: Comparative data base).
After the re-classification of the relevant indicators in a DPSIR framework (see Task 2), the indicators list is almost at the final stage (Annex 4: DPSIR indicators list). A final refinement (addition / exclusion of specific indicators, choice of measurement units, etc.) is needed following the feedback of ESS and SOGREAH. Once the indicators list is finalized, data from case studies need to be collected to complete the data base, asking for the contributions of SUMER and case studies partners.
TASK 2: ORGANIZE IN A DPSIR FRAMEWORKThe DPSIR framework has been chosen, as the most suitable approach to incorporate all main elements of the SMART project. In fact, it lets to take into account different scenarios, inputs from case studies to the models, outputs of the models, aggregated impact indicators, results of the comparative analysis and the effect of policies responses on the results. An interactive example of how the DPSIR framework interacts with the SMART approach is given in the attached PPT presentation.
During the last month, FEEM classified the indicators of the Comparative data base in the light of a DPSIR conceptual framework (Annex 4: DPSIR indicators list), following the framework adopted by the EEA Report “Sustainable water use in Europe”, where the DPSIR framework constitutes the basis of the analysis of the water resources situation in Europe. This classification implied a necessary re-adaptation of the initial indicators contained in the Comparative data base (Annex 3). Minor adjustments to the DPSIR indicators list are envisaged.
TASK 3: DEFINE SCENARIOSIn a DPSIR framework, SMART scenarios (BAU, positive and negative) can be defined by D indicators, which measure the forces that drive the pressures on the water system. In the DPSIR indicators list, D indicators have been classified in 3 broad categories, i.e. Population, Economic Development (Agriculture, Industry, Tourism) and the Climate and the Hydrologiycal cycle (Annex 4).
D indicators defining scenarios should be the inputs for WATERWARE and TELEMAC, together with additional information. Examples of these additional inputs are the irrigation systems used, the state of the water networks, the initial water allocation rules. For some of these additional models inputs (i.e. allocation rules) only the current situation is required for each of the different case studies, since eventual changes of the value of these indicators will be suggested from the models results. For
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other inputs (i.e. main crops), possible value changes following policy responses need to be suggested by case studies partners, eventually asking for the participation of stakeholders.
Measurement units for D indicators and other models inputs are proposed in the indicators list, but final decisions will be taken on the basis of data availability from case studies. Some data on D and additional inputs have already been collected during the exercise on scenarios definition. Each case study partner will be asked to provide the remaining ones, given that any case study can exclude those D and initial conditions that are not meaningful for them.
A problematic issue to be solved concerns the linkage between Scenarios and Responses. The crucial question is: do scenarios run indeipendently from Responses or do Responses determine the scenarios? The following two examples can better illustrate the issue:
EXAMPLE 1
Let’s consider the variable ‘irrigation water conveyance losses’. We have a baseline figure (current % of losses) and 3 scenarios (BAU (same %) optimistic (lower %) and pessimistic (higher %)). The options are the following:
the model is run based on the baseline figure. Only in the case that a Response is suggested about investing in the rehabilitation of irrigation networks, there is a value change of the baseline figure to the positive scenario figure and the model is run again, based on the new figure. The negative scenario is run to show what happens if the response is not implemented (and the state of the network worsens).
Models are run based on the 3 scenarios, independently from policy responses, to show what would happen if the losses were decreased and what if the irrigation system was not rehabilitated.
SCENARIOS: we run the model for all scenarios provided they are defined …); that does NOT mean that ALL parameters ust differ between these scenarios !EXAMPLE 2
Now consider the variable ‘total irrigated area’. We have a baseline figure (ha currently irrigated) and 3 scenarios (BAU (same ha), optimistic (less ha) and pessimistic (more ha)). The options are the following:
the model is run based on the baseline figure. Only in the case that a Response is suggested to adopt a land-use plan which converts some currently irrigated area into, let’s say, turist development area, there is a value change of the baseline figure and the model is run again based on the optimistic scenario, based on the new figure. The pessimistic scenario is not meaningful in this case.
The 3 scenarios are run anyway, independently from policy responses, to show what would happen to the system if the irrigated area was enlarged and what if it was reduced.
A feedback from ESS and SOGREAH is needed, in order to clarify:
- if the proposed variables (WHERE ARE THEY ?)for the definition of scenarios (D indicators + additional models inputs) are consistent with WATERWARE and TELEMAC
- The MODEL does not know anything about indicators; it only knows inputs and outputs; relevant inputs are:
1. The set of (demand) nodes considered in ech scenario
2. The parameters describing water demand (amount and pattern, strategies for allocation)
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This doe NOT translate directly into the indicator framework, but has to be extracted ( and classified/structured) a posteriori.
- the relation between scenarios and responses.
TASK 4: LINK SCENARIOS ASSUMPTIONS TO (P) INDICATORSP indicators refer to pressures on water resources and the environment, driven by the above mentioned Driving Forces, given the additional models inputs. In the DPSIR indicators list, P indicators have been divided into 4 main categories, i.e water demand, water supply, water abstraction and water pollution. P indicators can be derived from D indicators and additional models inputs either by models elaborations or expert system relations. See the fololowing examples:
EXAMPLE 3
GENERATION OF A P INDICATOR (URBAN WATER DEMAND) BY AN EXPERT SYSTEM RELATION
IF Urban growth rate (D) is between 2% and 3%
AND Annual per capita domestic consumption change (additional input) is between 0% and 1%
THEN Urban water demand change (P) = + 3%
While the rule-based methodology is attractive, the example would easily be implemented in a very simple decision table structure. RATHER THAN directly assuming changes in demands (expressed by a simple sceling factor in the model assuming constant pattern ?) we “assume more basic/primary data (like population growth based on the idea that this is somehow easier or more plausible ???) and DERIVE the water demand from that. THE MAIN IDEA is to illustrate consistency and plausibility.
EXAMPLE 4
GENERATION OF A P INDICATOR (NATURAL DROUGHTS) BY A MODEL ELABORATION (WW)
The P indicator “natural droughts” can be generated by the DROUGHT ASSESSMENT MODEL of WW, based on D indicators of the category “Climate and the Hydrologycal cycle”, plus some additional models inputs.
The DROUGHT ASSESSMENT modeule needs several decades of precipitation data; seems only feasible for Turkey given the current data situation !
A series of examples on how D indicators could be linked to P indicators have been prepared (see Annex 4 and PPT presentation). A feedback from ESS and SOGREAH is needed to determine which P indicators can be generated by WATERWARE and TELEMAC. Then, ES relationships for the generation of the remaining P indicators will be defined by FEEM and UATLA.
TASK 5: EXTEND INDICATOR LISTS BE MODEL OUTPUTSThrough Waterware and Telemac, D and P indicators are transformed in model outputs, which represent the State (S) in a DPSIR framework. S indicators collate information on the State of water resources with respect to quantity (demand/supply ratios) and quality (surface, groundwater and coastal water), as determined by Pressures. An additional category addressing the state of the land
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(desertification, coastal erosion, etc.) may be added, if feasible. S indicators are intended to be WW and TELEMAC models outputs.
EXAMPLE 5The elaboration by WaterWare of the P indicators influencing the supply of groundwater (i.e. droughts, run off, etc.) can provide the S indicator ‘Total groundwater supply’.
Again, a feedback from ESS and SOGREAH is needed to determine if the proposed S indicators (IS THAT WHAT YOU LIST IN APPENDIX 4 ?) can be generated by WATERWARE and TELEMAC.What you have listed there is, I THINK, insufficient: total supply is irrelevant unless we know whether that meets DEMANDS, reliably ??
TASK 6: DEVELOP AGGREGATE INDICATORS (NORMALISED) Through proper functions or expert system relationships, S indicators can be linked to Impact indicators (I), i.e. aggregate normalized indicators, whose main features were described in the document “The Role of Indicators in SMART” (Annex 5), circulated among the partners in early 2003.
I indicators refer to the consequences of State conditions on the 3 main spheres of human systems, i.e. the economy, the environment and the society. 7 main Impact indicators have been identified so far (see Annex 4), which slightly differ for the set proposed in the previous document. Some I indicators can be generated by WW, others may be derived by using simple functions, other will need more complex Expert System relationships.
EXAMPLE 6The demand-supply balance model of WW can generate the I indicator ‘Demand/Supply ratio for domestic use’ by combining the S indicator ‘Total groundwater supply’ with the P indicator ‘domestic water demand’, given water allocation rules. Then, this I indicator can be further elaborated by some ES relations, in order to obtain the average number of days per month in which drinkable water is not supplied. This I indicator can be seen as the Impact on the quality of life (thus addressing the social sphere).For that, no ES is needed: this is a direct WW result.BUT: if you want an indicator like: RELIABILITY OF WATER SUPPLY you may need a few rules to derive that across sectors, over time, considering some non-linear assessment, from model results.
Feedback from all partners is needed to agree on I indicators and on how to compute them.
TASK 7: MC/MO ANALYSIS (NON-DOMINATED ALTERNATIVES BASED ON PREFERENCE STRUCTURES)The comparative analysis of the 5 case studies will be based on I indicators, with the purpose to identify “winners” and best practices. The results of the Comparative Analysis will help in defining adequate Responses (R), jointly decided by analysts and stakeholders, for each case study, to help improve the negative Impacts for each case study through their influence on D, P or S indicators.
Possible methodologies for carrying out the comparative analysis have been defined and presented in the document “The Role of Indicators in SMART” (Annex 5).
IN PRACTICE, this will have to be AS SIMPLE and AGGREGATE (or exemplary) as possible.
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TASK 8: DEFINE A POLICY AND LOGICAL FRAMEWORK CAPABLE TO REPRESENT CASES OF INVESTMENT, RE-ALLOCATION AND RE-STRUCTURING, ETC.The policy framework needed to find adequate Responses and lessen negative impacts has to be defined with the help of stakeholders. The DPSIR logical framework allows to take into account all possible policy responses. In fact, Responses (R) can concern policies for regulating the population growth, or for planning the economic growth and land use; interventions addressing the water demand or water supply management or the integrated water resources management and allocation; actions taken to improve the wality of water. R can have a direct influence on indicators of D, P, or S, so as to change models inputs or outputs.
EXAMPLE 7If the I indicator “number of days per month without (WHAT WOULD “WITHOUT” MEAN ? NONE, less then 100%, less than 80% …??) drinking water supply” is >x, then an adequate Response can be the change in irrigation systems (i.e. on the demand side), so that groundwater abstraction per irrigation uses lowers (i.e. there is an effect on the P indicator ‘water demand for agricultural use’) and allocation rules can change in a way that more water is allocated for residential uses.
NOT THAT EASY: you have to consider BOTH absolute amounts and distribution over time; it does not really matter if there is no drinking water from 02:00 to 05:00, even every day !!! SO this is ONE reason to formulate more complex RULES to evaluate that.
Once adequate responses are identified, a second run of models can be done, in order to assess what is the effect of the Responses on the Impacts. Once the models have run again following the change in inputs due to Responses, it is important to assess whether these Responses have led to the forseen results, i.e. an improvement with respect to I indicators. The final I indicators from all case studies may then be subject again to a comparative analysis, in order to compare the effects of the application of best practices in the different case studies.This last step I am not sure I understand: you want to ITERATE with the models to try different RESPONSES to minimise IMPACTS ? (that is OPTIMA …)
TASK 9: COMPLETE DELIVERABLEThe deliverable on the Comparative Analysis of case studies will be completed jointly by FEEM and UATLA and will include most of the work presented in this internal report.
WHAT’S NEXTThe next steps include:
(a) Obtain a feedback from ESS and SOGREAH to understand if the exemplified interconnections of the DPSIR indicators with WW and TELEMAC inputs and outputs can work well as proposed, or how they need to be modified.
IF ANYTHING, SIMPLIFY.
EXAMPLE 8 (WW)
Let’s consider the I indicator ‘D/S ratio for agriculture’. Following the proposed DPSIR framework, this indicator is generated by WW, through the elaboration of:
- one P indicator (total water demand for agriculture);
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- another S indicator (total water supply: surface water + groundwater);
- an additional model’s input (current allocation share to agriculture).
- UNFORTUNATELY, this in itself is NOT a good indicator, since there are NUMEROURS (like infinte ?) ways to allocate water with the same TOTAL of VERY different utility !
The P indicator ‘total water demand for agriculture’ is also generated by WW through the elaboration of:
- a D indicator (total irrigated area);
- other model inputs (i.e. main crops, irrigation systems, water re-use, conveyance losses).
The S indicator ‘total water supply’ has been also generated by WW through the elaboration of:
- a D indicator (i.e. precipitation rate),
- some P indicators (i.e. droughts, run off, etc.),
- some other inputs data.
- DEPENDS; IF we (have enough data) and use the irrigation water demand model, then YES;
- Else, we just INPUT the water demand !
In this way, data needed from case studies are D indicators and additional model’s inputs, while P and S indicators are generated by WW.
NOT NECESSARILY, see above.
Is this as a correct representation of how WW can work and be related to a DPSIR framework? If not, how does it need to be modified?
WW uses a set of inputs and generated a set of outputs; these can, after a model run, be classified within the DPSIR framework in more than one way, depending on the conceptual model behind the assumptions and inputs (WHAT drives WHAT !)
EXAMPLE 9 (WW + TELEMAC)
Let’s consider the S indicator ‘coastal water quality’. Following the proposed DPSIR framework, this indicator is generated by TELEMAC, through the elaboration of:
- a P indicator (i.e. polluted water discharged into surface flows)
- other model’s inputs.
The P indicator ‘polluted water discharged into surface flows’ is generated by WW (water quality model) through the elaboration of:
- some D indicators (total population, total agricultural area, type of industries, etc.)
- some additional model’s inputs (diffusion of sewage treatment plans, diffusion and type of fertilizers, % of industrial wastewater treated, etc.)
IF we use WW to estimate coastal pollution (rather than a direct set of assumptions), we have two sets of inputs (and at least one model) 1) the FLOW of water generated by WW; the pollution load from the nearest relevant major source (city, irrigation district) a water quality model for the DOWNSTREAM section of the system from that source (like BLTM).
Is this as a correct representation of how TELEMAC AND WW can work and be related to a DPSIR framework? If not, how does it need to be modified?
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(b) Obtain a feedback from ESS, on how scenarios are linked to responses (see Examples 1 and 2)
THEY ARE NOT per se and intrinsically: WHAT you consider a RESPONSE (and what a primary pressure or state ??) is VERY ARBITRARY and depends on the explanatory model used !
(c) Finalize the list of DPSIR indicators and other models’ inputs data needed, on the basis of the feedback of ESS, SOGREAH
(d) Collect missing data for D indicators and other models’ inputs from SUMER’s database and case study partners (co-ordinated by UATLA?).
(c) Define functions and expert system relations to derive those P and S indicators that are not being generated from models (FEEM and UATLA, with the feedback from ESS and SOGREAH).
(d) Agree upon I indicators with all partners (co-ordinated by FEEM).
(e) Start working on the methodology for the comparative analysis (FEEM, ESS, UATLA).
I THINK THIS IS ENOUGH EXCUSE FOR A WORKING MEETING TO WORK ALL THIS THROUGH IN A NUMBER OF CONCRETE EXAMPLES, THEN SELECT A FEW CASES TO PURSUE IN DETAIL. WE WILL HAV ETO BE VERY PRAGMATIC, BUT OF COURSE THE REPORT CAN BE MORE AMBITIOUS THAN WHAT ACTUALLY CAN BE ACHIEVED FOR ALL CASES !
IN GENERAL:WW uses only few data types that represent (are the basis of) indicators:
- the STRUCTURE (hard to capture)
- - time series of water DEMAND, together with a simple multiplier for each to scale them more easily
- LOSS terms or coefficients that describe the efficiency of water use.
The OUTPUT is any temporal and sectoral integral of demand/supply ratios, which
Implies efficiency.
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1 ANNEX 1: LOGIC FRAME FOR THE DEFINITION OF SCENARIOS AND INDICATORS
A) SCENARIOS DEFINITION1) Define good and bad situations for your case study
SOME explanation what we mean by GOOD and BAD: enough water, not enough water.
Think about your case study. Try to determine what can be considered good situations and bad situations. You can also refer to the Key Water Issues identified in D01.1 (Requirements and Constraints Report), Chapter 3.2, Table 7.
Example: for the Gediz basin in Turkey, representatives of the General Directorate of DSI said that they consider being a bad situation an insufficient water availability per surface unit in irrigated areas.
2) Identify variables
For each good / bad situation you have determined, try to identify which variables could lead to it. These may be both independent from water policy decisions (i.e. demographic and economic trends, meteorological conditions), and directly dependent on water policy decisions (i.e. technological options and water allocation rules). You can also refer to the Key Issues of Change D01.1, Chapter 3.2, Table 8. Try to work backward as far as you can. For example, ask yourself what could lead to a certain demographic trend.
Example: what could determine an insufficient water availability per surface unit in irrigated areas in the Gediz river basin?
12Insufficient water availability for agriculture
High urban population growth rate
Development of tourism and / or industrial sectors
Unchanging or worsening precipitation patterns
Unchanged irrigation method (=flooding)
Increasing demand in urban sector
Decreasing total water supply
Increasing demand in tourism / industrial sectors
Unchanged water use per production unit
Increasing immigration from rural to urban areas
Increasing birth rate in urban areas
Land use change (from rain fed agricultural areas to industry or tourism)
3) Quantify, if possible, critical rates of change that could imply a major worsening / improving of the current situation
For some of the driving forces that you identified at stage 2), try to determine crucial rates that could lead from the current situation (baseline scenario) to a bad or good situation (pessimistic / optimistic scenarios). These crucial rates should be generally different from the actual rates of change, which will lead to the BAU (Business As Usual) scenario.
Example: for the Gediz basin, you should try to assess:
- current urban population: xxx (baseline scenario)
- actual annual growth rate of the urban population: 1.5% (BAU scenario)
- optimistic growth rate: 1% (optimistic scenario)
- pessimistic growth rate: 3% (pessimistic scenario)
4) Provide the list of variables and crucial change rates defining scenarios
Using the results achieved at steps 2) and 3), provide a table like in the following example.
Example: with respect to water availability for the agricultural sector:
VARIABLES / DRIVING FORCES
BASELINE BAU OPTIMISTIC PESSIMISTIC
Urban growth rate xxx thousands + 1.5% + 1 % + 3 %
Land use change in selected areas
Rain fed agriculture
Rain fed agriculture
Rain fed agriculture
Tourism / industrial development
Precipitations xxx mm/y + 0 % + 10 % - 10 %
Irrigation method flooding flooding sprinkling flooding
B) INDICATORS SELECTION5) Consider the social – economic – environmental consequences of the bad / good situations
identified in 1) and figure out how to measure them
Ask yourself why a previously identified bad (good) situation, i.e. with respect to water availability for agriculture, should be considered bad (good), from a economic / social / environmental perspective. Then, try to figure out how it could be possible to effectively measure these economic, social and environmental outcomes, given the available tools (Waterware, TELEMAC and the Socio-Economic Analysis). You can also refer to the first draft of possible indicators, which I gave you in Cesme. You will also find it within the attached document, “Framework for Indicators selection”, which also underlines the main criteria to be respected in the selection of indicators.
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Example: a insufficient water availability for agriculture in the Gediz river basin can imply:
- ECON: A decrease in the agricultural production and per capita income in the agricultural sector
How to measure it: link, using an algorithm, the available water in the agricultural sector to the agricultural production and monetize the change in production which follows a change in water availability.
- SOC: A high level of social conflict (farmers’ protests)
How to measure it: link, using an algorithm, the available water in the agricultural sector to the unemployment rate in that sector and compute the change in the unemployment rate due to a change in water availability. Then, using an expert system, set a relationship between the unemployment rate (quantitative) and other available socio-economic variables (identify suitable quantitative or qualitative variables) and the level of social conflict.
- ENVIRON: A bad level of environmental health (higher concentration of chemicals in the drainage water)
How to measure it: link, using an algorithm, the amount of drainage water in the agricultural sector to the average chemicals’ concentration and then set, using an expert system, a relationship between the chemicals’ concentration (quantitative) and other available variables (identify suitable quantitative or qualitative variables) and the quality of the environment (qualitative).
6) Summarize the results achieved at step 4) and provide the final list of indicators.
Make sure that for each proposed indicator, you have previously explain how it could be measured.
Example:
- Income loss in the agricultural sector
- Level of social conflict
- Environmental health
Final remarksThe expected outputs from this brain storming exercise are:
o The list of variables and crucial change rates defining scenarios (possibly organised in a table as in the example) – step 4.
o The list of indicators – step 3.
You can of course avoid to follow all the proposed steps and arrive to the final outputs in a different way.
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ANNEX 2: CONTRIBUTIONS FROM CASE STUDIES
EGYPTList of variables defining scenarios
Baseline BAU Optimistic Pessimistic
Population Resident population
Natural gross rate
Urban Gross
Rural Growth
Wastewater
3.0 %
2.8 %
3.0%
-1%
Shortage of infrastructure
2.8%
2.7%
2.6%
1%
Development of a wastewater network
3.2%
3.0%
3.2%
-2.0%
Increased rate of water quality deterioration
Industry Wastewater industrial pollution
National regulations not fully enforced
Better control Ineffective control
Agriculture Drainage systems
Surface irrigation
Excessive Fertilization
Threatened by increasing salinity
Successful limitation
Drip irrigation
Upgrade awareness
Increased salinity
Increased loss of agricultural land
Land use Changes
Residential growth
Aquaculture development
Unplanned Plans and better control
Increased loss of land
Tourism Shortage of Institutional capabilities
Loss of sites
Deterioration of monuments
Implementation of a national plan
Continued loss of resources
Climate Change
Salt water intrusion
Coastal erosion
Increased salt water intrusion and erosion
Efficient control
Better awareness
Continued loss of resources
Water quality
Pollution
Salinity
Increased pollution
Increased salinity
Limiting pollution
Shortage of High quality water for sustainable development
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1.1 JORDAN
1. Driving Forces / Variables1. Growing Demand for Domestic Purposes
2. Tourism
3. Industry
4. Transport
5. Agriculture
6. Water Supply
7. Demographic change
8. Land use change
9. Technological change
10. Institutional Change
11. Economic trends
12. Water allocation Rules
13. Meteorological conditions
2. Defining Bad and Good Situations for the Aqaba Region
2.1. Bad Situations1. 1.1 MCM/year of reclaimed wastewater is reused near the coast result in:
a. Risk of Pollution
b. Restricted Agriculture
2. Population of Aqaba will increase ( regular increase, and irregular increase due to new job opportunities resulting from expanding industry, tourists, and other activities)
3. The increase in tourist rate will increase demand for guaranteed water quality for protection of biodiversity and the safety of bathing waters.
4. Uncontrolled tourist activities cause damage to corals by tourist boats, coral breakage by divers.
5. 20 MCM/year is imported from adjacent aquifers that will cause draw down in groundwater level.
6. There are major industries located along the coastline of the Gulf of Aqaba that causes environment damage including emission of pollutant gases and pumping of cooling water back into the gulf. Any expansion in the industrial facilitates will cause more stress on the environment of Aqaba.
7. New industries are anticipated to be developed in the future such as Hasad Liquid Fertilizer, Kemira Arab Potash Company, and Lumber Factory. These industries will increase water uses for industry, and thus increase the stress in the water demand
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8. Three main ports are operating to import and export various products including phosphates, potash, fertilizers and oils. An environment problem in Aqaba is phosphate dust emerging from loading and unloading activities, this dust will
Eventually sink in the water being difficult to dissolve it precipitates on the corals resulting in decrease in coral growth around phosphate port.
9. Movement of vessels brings sources of pollution to the gulf from solid waste, leakage of oils and anchorage.
10. The new development plan for Aqaba suggests the transfer of the main port to the southern beach; this will cause a heavy stress on the corals reef in the southern beach, which comprises the highest coral diversity and uniqueness.
11. Establishment of Aqaba special economic zone resulted in:
a. Attracting new investors in trade and industry, this development will increase demand for water supply for the growing population and the future industrial activities, and
b. Higher rate in construction and building practices
c. On the other hand will lead to increase in wastewater generation.
2.2. Defining Good Situations 1. Introduction of desalination units will increase the amount of available water.
2. Declaring The Aqaba area as a special economic zone resulted in attracting new investors in trade and industry, and increasing in the tourist activities, therefore improving economy.
3. The Gulf of Aqaba hosts an extraordinary diversity of corals and related marine life, which attract a large number of tourists to Aqaba and thus development of new hotels and resort cities resulting in improving the economy. Approximately 66% of Jordan tourists visit Aqaba, about 600,000 tourists (1996).
4. The Anticipated developments in the Tourist and industry sector will bring new job opportunities.
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2.3 Scenarios and indicators
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1.2 LEBANON
Management, Social Planning & Policies
VARIABLES/
DRIVING
Forces
BASELINE BAU OPTIMISTIC PESSIMISTIC
Urban growth rate 80 000 1.97% 1.5% 3%
Integrated National Master Land use Planning
10 % of country On-going project of comprehensive land use planning (SDATL)
Full implementation of the SDATL with a focus on the coastal zone and control of water and sea pollution
Partial implementation of SDATL
National environmental policies and programs
Exist in established codes only
Restricted attempts, programs with random international and bilateral funding, implemented according to priorities of the donor organizations and countries.
. Adopt a national policy for the environment
. Organize and activate the MoE
. Action plan for the implementation of conventions ratified by Lebanon mainly on desertification...
Partial implementation
Regulations and law enforcement, covering many aspects of environmental management
Many are old & not enforced
. Low levels of regulatory enforcement . . . Lack of precision in the law
. Unclear role of responsibilities
Ratify fully code of environment & implementation
Ratify & implement partially code of environment
Regulations to control damages caused by industry emissions
Survey covered industries & causes assigned
. Master plan regulating the polluting industries
. Uncontrolled discharges of industrial emissions (liquid, solid and gaseous) without any form of treatment
Effective and enforceable industrial pollution control regulations.
Ineffective control measures & monitoring
Regulations to control damages caused by
Ineffective control
Rational use of agro-chemicals (pesticides and
Set standards for the use of pesticides,
Ineffective control measures &
21
excessive use of pesticides and fertilizers
fertilizers) are still scant or localized
fertilizers and hormones
monitoring
Solid waste management
Highly variable & local mostly non-effective solutions; minimal separation, composting and recycling
. Landfill for solid waste. Uncontrolled disposal of sewage in some areas. Raw sewage is discharged into rivers and sea. Partial modern techniques
. Rehabilitation of disposal sites . Set a national waste management plan that adopts integrated approach and defines appropriate solutions for each region
. Adopt a municipal waste disposal
. Decrease the quantity of waste at the source
Adopt incineration & land filling
Human and economic loss due to natural disasters
Mostly unplanned solutions
Restricted emergency measure
Comprehensive Natural Disaster Reduction Management Plan (CNDRMP)
Ineffective partial plan
Expenditure on Research and Development as percent of GDP
Less than mandated
0.0001 % of GDP 5 % of GDP 1 % of GDP
Environmental protection expenditures as a percent of GDP
Not even mandated
Restricted calls for law
Full mandate in accordance with sectoral need
Partial sectoral restrictions
22
23
Climate Change
24
25
Technology
Variables /
Driving Forces
Baseline BAU Optimistic Pessimistic
Desalinizationprocess
0 m3 0 m3 106 m3 0 m3
Small Dams 106 m3 2 x 108 m3 109 m3 106 m3
Recycling of Waste water
0 m3 0 m3 106 m3 104 m3
Irrigation method
flooding flooding Sprinkling Flooding
Integrated management
Non-existent Non-existent Covering 50 % of sector
Covering 20 % of sector
Ground water exploitation
Insufficient use
20%-40% 60% 30%
Balance supply-demand
Partial attempts
Restricted areas
In 75 % of area
30 %
26
27
1.3 TUNISIAList Of Variables Defining Scenarios
Variable / deriving forces
Baseline BAU Optimistic Pessimistic
Population 68.710
Resident population annual increase
Natural growth rate
Urban population growth
Rural population growth
3.1%
1.15%
2.8%
1.5%
2.5%
1 %
2.5%
1%
3.5%
1.5%
3%
2%
Industry Waste water
Water consumption
Strict national regulations
1.2MCM
Effective control
1 MCM
Ineffective control
1.5MCM
Water policies Agricultural water subvention
Taxes (tourism and industry)
Sustainable development and mobilization strategy for surface water.
National master land use planning
Overexploitation
Groundwater overuse
National policy for sustainable development
Control the conflict between the agriculture and touristic sectors (in terms of surface)
Limit squandering
Stop groundwater use
Fully applied
Equilibrium
Lack of control
Lack of control
Partially applied
Conflict increasing
Tourism : 70 units
National policy for tourism development
High coastal touristic units density
subvention from the
+0%
Limit the subventions
+10%
Absence of limitation
28
Water overexploitation (in spite of the heavy taxes)
national and private sectors
awareness national policy to limit water squandering
Successful measures
unsuccessful measures
Agriculture : Irrigation techniques
Irrigated surface
Land use
Irrigation techniques improvement
Threatened by construction (private houses).
Irrigated cultures
Successful
Limitation & control
Limit water squandering
Successful
Unsuccessful
measures
Increasing demand for water
Water resources: Surface water
Ground water
Import
Losses
Water mobilization policy
Overexploitation
Insufficient imported quantities
25% (network)
Increasing water mobilization rate.
Stop ground water overexploitation
Increasing imported quantities
10%
Constant
Lack of control
Constant
25%
Water quality :
Pollution
Salinity
Monitoring measures
Salt water intrusion
Efficient measures
Control (ovoid ground water overexploitation, table refill,…)
Inefficient
Lack of control
29
Immigration /Immigration /emigrationemigration
Population Industry + Tourism
AgriculturePolicies Water Quality Water Ressources
Urban population Rural population Natural growth rate Water consumption National policiesfor tourism development
Surface water Import Ground waterPollution Salinity Losses(Network )
Subvention Taxes Mobilization for surface water
Irrigation techniques
Land use change
Insufficient Insufficient wawaterter supplysupply
IncreaseIncrease in in WaterWater demanddemandLack of controlLack of control IncreaseIncrease in in
WaterWater demanddemandDeclineDecline in in
ressources ressources quantityquantity
Irrigatedsurface
Increasing demand for
water
Increasing Increasing demand for demand for
water water
LackLack ofof suitablesuitablewaterwater
Reduce in precipitation rate
Prohibitedperimeters
Immigration /Immigration /emigrationemigration
Population Industry + Tourism
AgriculturePolicies Water Quality Water Ressources
Urban population Rural population Natural growth rate Water consumption National policiesfor tourism development
Surface water Import Ground waterPollution Salinity Losses(Network )
Subvention Taxes Mobilization for surface water
Irrigation techniques
Land use change
Insufficient Insufficient wawaterter supplysupply
IncreaseIncrease in in WaterWater demanddemandLack of controlLack of control IncreaseIncrease in in
WaterWater demanddemandDeclineDecline in in
ressources ressources quantityquantity
Irrigatedsurface
Increasing demand for
water
Increasing Increasing demand for demand for
water water
LackLack ofof suitablesuitablewaterwater
Reduce in precipitation rate
Prohibitedperimeters
30
TURKEYVariables/driving forces
Baseline BAU Optimistic pessimistic
Birth control policy
existing existing existing existing
Urban growth rate 923 000 1.5 %/y 1 %/y 3 %/y
Rural growth rate 704 000 - 1 % /y -1 % /y -2 % /y
Precipitation rate 700 mm/y 0 % 0% -10%
Groundwater supply
9 mm/y 0 % 0 % - 10%
Surface water supply
59 mm/y 0 % 0 % - 10%
Groundwater pollution
Class IV Class IV Class III Class IV
Basin-out water supply (groundwater)
0.2 mm/y 0.2 mm/y 0.4 mm/y 0.5 mm/y
Domestic water use
7.4 mm/y 0% 0.5 %/y 3.5 %/y
Industrial water use (groundwater)
3 mm 0% 5%/y 10%/y
Irrigation water use
39 mm 0%/y - 20% 15%
Domestic water supply investments
sufficient sufficient sufficient insufficient
Change in crop pattern
Cotton, grape, corn
Cotton, grape, corn
Grape, vegetable,corn
Cotton, grape
Irrigation m/o investments
insufficient insufficient Sufficient insufficient
Loss rate in irrigation system
30 % 30 % 5% 30%
Irrigated area 1070 km2 0% 0% 1%
Industrial water use (surface water)
0 mm 0 mm 0 mm 2 mm
Surface water quality
Class IV Class IV Class III Class IV
Water exploitation awareness
Insufficient awareness
Insufficient awareness
Comprehensive awareness
Insufficient awareness
31
32
Immigration from rural to urban areas
Population
Growth
Increase in Urban
Population
Basin-out water supply
Over Exploitation
of Groundwate
r for irrigation
Increase in
Industrial Ground & Surface
Water Use
Groundwater Pollution
Low Precipitatio
n
Insufficient Financial Status
for Domestic Water Supply Infrastructure Investments
INCREASE IN DOMESTIC
WATER DEMAND
INCREASE IN IRRIGATION
WATER DEMAND
INCREASE IN INDUSTRIAL
WATER DEMAND
Change in Crop
Pattern
Increase in Irrigated
Area
Surface Water
Pollution
Insufficient Maintenance&
Operation
No new investment
for Irrigation
Water Conveyan
ce Systems
No new investment for better irrigation methods
High irrigation
water loss
INSUFFICIENT WATER SUPPLY
ANNEX 3: COMPARATIVE DATA BASE
34
2 ANNEX 4: DPSIR INDICATORS LIST
35
36
37
ANNEX 5: THE ROLE OF INDICATORS IN SMART
1) INTRODUCTION...............................................................................................................40
2) CHRONOLOGICAL DEVELOPMENT OF THE INDICATORS’ ISSUE WITHIN THE SMART CONSORTIUM..............................................................................................................................................................41
3) SMART IN A DPSIR FRAMEWORK: WHERE DO INDICATORS FIT?...........................................................................................................41
4) CHOOSING PROPER INDICATORS FOR THE COMPARATIVE ANALYSIS..................................................................................45
4.1 CRITERIA FOR THE SELECTION OF PROPER INDICATORS 454.2 A FIRST DRAFT OF POSSIBLE INDICATORS 46
5) METHODOLOGICAL OPTIONS FOR WP 10...............................................49
1. Introduction
The objective of WP 10 is the comparative analysis of the individual case study results, across the five case studies, against other comparable projects and against the relevant European policies in this domain.
More specific objectives are as follows:
To identify commonalties and differences and relate them to the specific regional setting;
To identify more generally applicable results that are invariant across the case studies;
To organize these findings in terms of a comparative policy assessment and best practice examples.
When dealing with comparative studies, the first step is certainly to decide what are the relevant variables that can constitute the basis of the comparison. These variables have to be meaningful, both individually and as a whole, and measurable, in order to come up with a noteworthy comparative assessment. This is why the 1st task of WP 10 is to organize the individual case study results in a common conceptual framework of common indicators of sustainable coastal zone development and resource management.
The selection of such indicators is not straightforward, although a huge set of indicators for sustainable development, for water or for marine environment and coastal zones is available from very notable sources like Plan Bleu (Report on 130 Indicators for Sustainable Development in the Mediterranean Region), the European Environmental Agency and EC / Eurostat. Actually, most of these indicators can be considered without any doubts as being meaningful, but not always they are measurable, given the existing data sources of the five case studies and the outputs provided by the software, i.e. WATERWARE and TELEMAC, which have been chosen to analyze the scenarios.
The consortium of SMART project, and notably ESS, UATLA and FEEM, have been involved in the literature review and selection of relevant indicators for the last six months. The chronological development of the work on indicators which has been done so far, is briefly described in Chapter 2. Chapter 4 presents the final step of this process, i.e. a first draft of indicators which could be used for the comparative analysis. This list is the result of a brain storming exercise carried out during a FEEM – ESS meeting, which took place in Vienna in July. This first draft should be discussed, modified and integrated by the whole Consortium. 1
During the process, it was agreed by the involved partners, that a DPSIR conceptual framework should be adopted when dealing with indicators. Chapter 3 will illustrate how indicators can fit this framework. The DPSIR conceptual framework seems to be suitable to respond to the 2nd task of WP 10, i.e. to analyse the individual case studies within the selected framework. Hence, the analysis of the five case studies under the three different scenarios is likely to be done within the logical frame of DPSIR, by using one or more methodologies, which will be briefly described in Chapter 5.
1 In order to lead the partners in this exercise, a Logic framework for scenarios and indicators has been developed by FEEM.
40
The 3rd task of WP 10 is to identify and report common trends and best practice examples. This will be possible by analysing the results of the comparative analysis, which will provide some indications about best practices in water management (technologies and allocation rules), as well as about the demographic and economic patterns which are likely to better meet social, economic and environmental constraints. Then, the final result is likely to be a score from the most to the least sustainable alternative, where each alternative is meant to be one particular scenario applied to one particular case study.
2. Chronological development of the indicators’ issue within the SMART Consortium
During the first year of the SMART project a very close collaboration has been carried on between ESS and FEEM for the selection of a set of indicators that could be relevant for each case study area.
On March 2003 a first list of indicators was provided by ESS (Sandra Mink). The indicators were grouped according to the four categories mainly used for characterising sustainable development (environment, society, economy and governance) and they were selected taking into consideration the above mentioned sources (Plan Bleu, European Environmental Agencies and EC/Eurostat).
This first list of indicators was very broad in order to offer a wide choice of parameters that could be a useful starting point for defining later a specific set of indicators linked to the common characteristic detected in the case study areas.
Therefore the second list provided by FEEM has been an attempt to choose, within the list provided by ESS, the most suitable indicators for identifying a sustainable water resources management. Such indicators were grouped according to the common issues (water quantity, water quality, demographic change, land use change, technological change and institutional change) to each case study that have been identified in D0.01 (Requirements& Constraints Analysis). This second list tried also to integrate the indicators provided by UATLA (in the report on methodology and data requirements of March 2003) for carrying out the socio-economic analysis. A third document on indicators has been prepared by ESS on June 2003 and its main aim was the transposition of the selected indicators in the DPSIR framework. A meeting in Vienna on July 2003 has been organized to finalize the exchange of ideas carried out on indicators and to find out a fourth draft set of indicators suitable for doing the comparative analysis. The proposed draft set underpins a slightly different logic than the previous ones, since it has been built with the specific aim to form the basis of the comparative analysis and thus it has attempted to respect the criteria reported in Chapter 4.1. The following Chapters discuss the proposed fourth set of indicators in detail. From now on, the word ‘indicators’ refers to the 4th list.
3. SMART in a DPSIR framework: where do indicators fit?
All the work done so far on indicators has been analysed by FEEM with the attempt to organize it in a logical frame, which is coherent with the structure of the SMART project itself. The result of this work is showed in Figure 1. The figure shows that many variables are involved in all phases of the project: some of them enter as inputs of the software and the socio-economic analysis, then they are elaborated in order to come up with some outputs, for each of the three supporting columns of the project. Finally, these outputs have to be processed so as to produce relevant indicators for the comparative analysis. It is important to underline that this and the following graphs only contain a rough subset of the variables that are going to be used throughout the SMART project.
41
Economic
COMPARATIVE ANALYSIS
TechnologyINPUTS
Hydro -meteorological
Bathymetrical
Pollutants characteristics
TELEMAC
OUTPUTS
Geographical &
Hydrological
INPUTS
External variables
Decision variables
WATERWARE
OUTPUTS
Water demand
Waterallocation
INPUTSDemographic
Socio-Economic
SOCIO-ECONOMIC ANALYSIS
OUTPUTS
Water use
SCENARIO
Institutional
INDICATORS INDICATORS INDICATORS
Hydrodinamicinformation
For each N, foreach t:
For each N, foreach t:
Demand/ supply ratio
Costs
BenefitsWater quality
Economic
COMPARATIVE ANALYSIS
TechnologyINPUTS
Hydro -meteorological
Bathymetrical
Pollutants characteristics
TELEMAC
OUTPUTS
Geographical &
Hydrological
INPUTS
External variables
Decision variables
WATERWARE
OUTPUTS
Water demand
Waterallocation
INPUTSDemographic
Socio-Economic
SOCIO-ECONOMIC ANALYSIS
OUTPUTS
Water use
SCENARIO
Institutional
INDICATORS INDICATORS INDICATORS
Hydrodinamicinformation
For each N, foreach t:
For each N, foreach t:
Demand/ supply ratio
Costs
BenefitsWater quality
The same information summarised in Figure 1 can be re-organised by using the DPSIR conceptual framework. Regarding the sustainability indicators and the related framework, using the DPSIR model (Driving forces, Pressures, State, Impacts and Responses) helps to a better understanding of the cause-effect relationships between the different interacting components of social, economic and environmental issues implied in the water resource management.
Figure 2 shows the traditional graphical representation of the DPSIR model. Driving forces, Pressures, State, Impacts and Responses are elements of a chain, whose rings are linked in a causal way. Regarding the sustainability indicators and the related framework, using the DPSIR (Driving forces, Pressures, State, Impact and Responses) model helps to understand the complex relationships between the drivers of environmental problems, their impacts and society’s responses to them.
Figure 1: Indicators within the SMART logical structure
42
Figure 4 shows how the main components of the DPSIR model interact in the structure of the SMART project. Inputs of the software and the socio-economic analysis include the first two elements of the cause-effect chain, i.e. Driving Forces and Pressures. The outputs of the three columns (WATERWARE; TELEMAC and the Socio-Economic Analysis) can be seen as the resulting State of the environment, in terms of water quantity and quality. Next, the indicators to be used for the comparative analysis should give a measure of the Impacts that a change in the State led by Driving forces through Pressures produce on social, economic and environmental spheres. Finally, the results of the comparative analysis should give indications about the best Responses that assure the highest level of sustainability for each case study. These Responses mainly concern technological options, institutional issues and water allocation rules, which can influence either Driving Forces or Pressures, in order to lessen the Impacts.
Responses
These impacts finally lead to responses by the society
Driving ForcesSuch indicators measure the forces that drive the actual water demand
PressuresSuch indicators collate information on pressures on water resources and the environment through the abstraction of water resources
StateSuch indicators collate information on the state of the water resources and supply system as determined by Pressures
ImpactsSuch indicators refer to the consequences of changes
in a state condition
origin of influence changes cause
Figure 3 : The DPSIR Framework
43
Figure 4: Indicators within the DPSIR conceptual framework
44
4. Choosing proper indicators for the Comparative Analysis
4.1 Criteria for the selection of proper indicators
The work carried out by the consortium on indicators so far is now to be reconsidered in the new light of the comparative analysis. The final result has to be a brief set of indicators (max 10), which should be:
1) Individually meaningful, i.e. capable to provide, individually, a useful and complete information on one aspect of sustainability. Therefore, in order to be general enough, each indicator should aggregate a set of more specific indicators, indexes, data.
2) Globally meaningful, i.e. capable to cover, globally, any aspects of sustainability of any given scenarios. Graphically, each corner of the sustainability triangle should be filled in with 2-3 indicators.
3) Feasible, i.e. capable to be built by using as unique source of information the software's outputs WATERWARE and TELEMAC (eventually supported by an EXPERT SYSTEM) and the results of the socio-economic analysis.
45
4.2 A first draft of possible indicators
Table 1 summarizes the first set of impact indicators that could be used for the comparative analysis. These indicators try to fulfil the three criteria mentioned in the Introduction (Individually meaningful, globally meaningful, feasible). In particular, Table 1, describes the meaning of each proposed indicators, thus trying to address the requirement for indicators of being individually meaningful, i.e. capable to provide, individually, a useful and complete information on one aspect of sustainability.
Table 1: First draft of Indicators for the Comparative Analysis
46
Table 2 addresses the feasibility criterion, by briefly describing the source of each indicator and a rough idea on how to measure it.
INDICATOR SOURCE HOW TO MEASURE IT GLOBAL or SECTORAL
Coastal Water Quality TELEMAC By aggregating the information on coastal water quality and deriving a single indicator from the time and spatial series provided by TELEMAC
Global
Environmental Health WATERWARE From a quantity point of view, by computing the residual water volume, after the demand has been satisfied (the residual can be an indicator of environmental health, since it serves for recharges, wetlands, etc.).
Global
Satisfaction of Water Demand
WATERWARE By aggregating information on the demand/supply ratio both temporarily and spatially.
Sectoral
(Agriculture, Industry, Urban, Tourism)
Cost-benefit ratio WATERWARE By computing costs due to the chosen technology and water allocation system and dividing them by the monetized benefits (i.e. agricultural production, which is possible given a certain amount of water and the chosen technology)
Sectoral (A, I, U, T)
Level of social conflict WATERWARE By computing the change in the level of unemployment generated by a change in water allocation. Unemployment is the consequence of an insufficient supply of water with comparison to the demand (i.e. farmers are forced to leave their fields if water is not sufficient for irrigation).
Sectoral (A, I, U, T)
Efficiency of the system
WATERWARE By computing production per water mc, given a certain water availability and the chosen technology.
Global
47
Figure 5 shows how these indicators can be seen with respect to the sustainability triangle, i.e. how the whole set addresses the criterion of being globally meaningful, i.e. capable to cover, globally, any aspects of sustainability of any given scenarios.
This indicator set needs to be reviewed by the whole SMART Consortium, in order to be refined and improved.2 In order to lead the partners in this exercise, a Logic framework for scenarios and indicators has been developed by FEEM (see the attached file).
2 See footnote 1
Figure 5: Indicators within the classical Sustainability representation
ENVIRONMENT SOCIETY
ECONOMYCost benefit ratio
Efficiency of the system
Coastal water quality
Environmental Health
Level of social conflict
Satisfaction of water demand
48
5. Methodological options for WP 10
Once the indicators for the comparative analysis have been selected, the case studies with respect to each scenario have to be analysed by adopting one or more suitable methodologies.
Some possible options for the methodologies are as follows:
1. Multiple Criteria Decision Analysis (MCDA), in which a comparison of some given alternatives
is developed with respect to some CRITERIA, which would correspond to the chosen indicators. In SMART, the alternatives would be the 5 case studies multiplied by the 3 scenarios per each case study. Hence, it would be possible to compare 15 alternatives with respect to some criteria (max 10), in order to derive some indications about best practices in water management (technologies and allocation rules), as well as about the demographic and economic patterns which are likely to better meet social, economic and environmental constraints. For doing so, a suitable MCA software should be adopted.
2. EXPERT SYSTEM, which implies the definition of “rules”, in a descriptive way, by using qualitative performances of each indicator. A first step consists in finding the way to transform numerical performances (i.e. pollutants concentration values) into qualitative ones (low, medium, high water quality). This can be done by means of statistical tools (mean, standard deviation, etc.). Once the qualitative ranges are defined for all indicators, they need to be combined to achieve a measurement of sustainability.
Example:
IF water quality >low
AND satisfaction of water demand >=medium
AND…
THEN sustainability degree= medium
3. A Multiobjective Methodology, based on the concept of the distance of any given scenario from an “utopia point”, where all the indicators have the best performance.
During the ESS – FEEM meeting in Vienna it was suggested to adopt at least two different methodologies for the Comparative Analysis. This is likely to imply that there will be different and possibly contrasting indications with respect to the degree of sustainability of each scenario. Hence, a platform for a discussion among different stakeholders will result, in order to enhance participation in selecting and justifying “best practice examples” (Task 3: To identify and report common trends and best practice examples). FEEM’s expertise is particularly suitable for carrying out the analysis by using methodology 1.
49
