1. Bulgarian Academy of Sciences. 22 July, 2008 1 Index Introduction Outline of the scheme Step 1. Individual weights Step 2. Preference aggregation Step 3. Determination of the indicators Step 4. Final aggregation Conclusions End

2. Bulgarian Academy of Sciences. 22 July, 2008 2 Introduction Sustainable development (Brundtland Commision, 1987): development that meets the needs of the present without compromising the ability of future generations to meet their own needs. This is, by nature, a multicriteria concept.

3. Bulgarian Academy of Sciences. 22 July, 2008 3 Introduction Sustainability Social EconomicEnvironmental

4. Bulgarian Academy of Sciences. 22 July, 2008 4 Introduction Natural capital vs. Man-made capital. Weak sustainability. Total capital constant. Substitutability paradigm. Strong sustainability. Natural capital and man-made capital are (at the most) complementary. Non substitutability paradigm.

5. Bulgarian Academy of Sciences. 22 July, 2008 5 Introduction Life cycle assesment. Environmental performance of production and services through all phases of their life cycle (from craddle to tomb): Extracting and processing raw materials; manufacturing; transportation and distribution; use, reuse and maintainance; recycling; final disposal. How to measure sustainability?

6. Bulgarian Academy of Sciences. 22 July, 2008 6 Introduction Ecological footprint. Estimate of the ammount of land area a human population, given prevailing technology, would need if the current resource consumption and pollution by the population is matched by the sustainable (renewable) resource production and waste asimilation by such a land area. How to measure sustainability?

7. Bulgarian Academy of Sciences. 22 July, 2008 7 Introduction (Urban) Indicators. A set of magnitudes measuring different concrete aspects of sustainability. Over 200 indicators are presently used. Still to be done: –To define a full common framework (meningful and comparable), –To actually measure them, –To develop synthetic urban sustainability indicators. How to measure sustainability?

8. Bulgarian Academy of Sciences. 22 July, 2008 8 Introduction... define a methodology, based on the reference point approach, to develop a pair of urban synthetic sustainability indicators (weak and strong) for a set of municipalities of Andalucía, based on a pre-defined set of indicators. In this work, we...

9. Bulgarian Academy of Sciences. 22 July, 2008 9 Outline of the scheme Data selection 0 Determination of individual weights 1 Preference aggregation 2 Synthetic indicators within each class 3 Final aggregation 4 -Municipalities -Indicators -Criteria -Experts Haldi (1995)Meta-Goal Programming Rodríguez et al. (2000) Reference Point Wierzbicki (1986) Strong and Weak Indicator

10. Bulgarian Academy of Sciences. 22 July, 2008 10 Municipalities. 18 (M) Andalusian municipalities, over 55,000 inhabitants. Indicators. 4 classes: –Environmental (13) –Urban development (12) –Demographic (16) –Economic (22) (I - number of indicators in a given class) Outline of the scheme

11. Bulgarian Academy of Sciences. 22 July, 2008 11 Outline of the scheme ENVIRONMENTAL CLASS WATER CYCLE % of water losses in the pipe line Water consumption (per inhabitant) Km of water supply line Km of drainage line ENERGY Electricity consumption (per inhabitant) MATERIALS CYCLE Volume of waste (per inhabitant) Paper containers (per inhabitant) Volume of glass recycled (per inhabitant) NOISE Day noise Night noise ATMOSPHERE Atmospheric inmissions Greenhouse efect emissions Global emissions

12. Bulgarian Academy of Sciences. 22 July, 2008 12 Criteria. The indicators are to be maximized or minimized –Some are clear (e.g. % of water loss) –Others are not so clear (e.g. Paper containers/inhabitant, electricity consumption) Panel of experts. 6 experts (ND): –2 Environmental –2 Social –2 Economic Outline of the scheme

13. Bulgarian Academy of Sciences. 22 July, 2008 13 1. Individual Weights Each expert k (k = 1,..., ND) assigns weights to the indicators in the following way: Assume a class of indicators is chosen, which contains I indicators. The expert classifies the indicators into L sets (VI, CI, I, NVI, NI is suggested)

14. Bulgarian Academy of Sciences. 22 July, 2008 14 1. Individual Weights For each l = 2,..., L-1, the expert is asked to place set l between sets l-1 and l+1. l - 1 l + 1 0 0.25 0.5 0.75 1 set l alkalk

15. Bulgarian Academy of Sciences. 22 July, 2008 15 1. Individual Weights The following system of equations is solved: The weights are assigned:

16. Bulgarian Academy of Sciences. 22 July, 2008 16 1. Individual Weights ENVIRONMENTAL CLASS I1I2I3I4I5I6I7I8I9I10I11I12I13 DM130.00100.0010.000.00100.0030.00 60.0030.0060.00 DM250.0075.0050.0075.00100.0050.00 75.00 100.00 DM3100.0050.0025.0050.00 100.0050.00 DM4100.0025.00 50.00 100.00 50.00100.00 DM5100.00 75.00 25.0050.00 25.00 DM650.00100.000.0025.00100.0025.0075.00 100.00 75.00 Weights for the environmental class:

17. Bulgarian Academy of Sciences. 22 July, 2008 17 2. Preference Aggregation We establish the following set of goals: The achievement function takes the form:

18. Bulgarian Academy of Sciences. 22 July, 2008 18 Best maximum deviation: 2. Preference Aggregation (AP1) d*, s max

19. Bulgarian Academy of Sciences. 22 July, 2008 19 Best total deviation: 2. Preference Aggregation (AP2) s*, d max

20. Bulgarian Academy of Sciences. 22 July, 2008 20 Pay-off matrix: 2. Preference Aggregation BestWorst Max. dev.d*d*d max Agg. dev.s*s*s max Meta-Goals: we choose values

21. Bulgarian Academy of Sciences. 22 July, 2008 21 Meta-Goal Programming Problem: 2. Preference Aggregation

22. Bulgarian Academy of Sciences. 22 July, 2008 22 An auxiliary problem is solved. The process can continue until we achieve a satisfactory solution. The final result gives the group weights for each class of indicators. 2. Preference Aggregation

23. Bulgarian Academy of Sciences. 22 July, 2008 23 2. Preference Aggregation BestWorst Max. dev.275.00310.00 Agg. dev.1470.001540.00 Group weights for the environmental class:

24. Bulgarian Academy of Sciences. 22 July, 2008 24 2. Preference Aggregation ENVIRONMENTAL CLASS I1I2I3I4I5I6I7I8I9I10I11I12I13 DM130.00100.0010.000.00100.0030.00 60.0030.0060.00 DM250.0075.0050.0075.00100.0050.00 75.00 100.00 DM3100.0050.0025.0050.00 100.0050.00 DM4100.0025.00 50.00 100.00 50.00100.00 DM5100.00 75.00 25.0050.00 25.00 DM650.00100.000.0025.00100.0025.0075.00 100.00 75.00 Group100.00 25.0050.00100.0065.0075.00 32.5075.00 Group weights for the environmental class:

25. Bulgarian Academy of Sciences. 22 July, 2008 25 3. Determination of Indicators For a given class of indicators, is the value of indicator i for municipality j

26. Bulgarian Academy of Sciences. 22 July, 2008 26 Aspiration and reservation levels: 3. Determination of Indicators

27. Bulgarian Academy of Sciences. 22 July, 2008 27 Individual achievement functions: 3. Determination of Indicators 0 1 2

28. Bulgarian Academy of Sciences. 22 July, 2008 28 3. Determination of Indicators Mun Individual Achievement Functions 12345678910111213 10.050.092.00 0.930.79 0.39-0.121.521.361.86 22.001.49-0.171.170.990.81.580.210.70.381.070.91.01 30.540.79-0.570.450.870.260.681.460.21-0.350.10.760.86 40.591.550.440.680.980.61.552.000.01-0.420.61.341.72 50.770.96-0.720.130.970.520.940.470.781.120.060.460.51 6-0.281.52-0.34-0.191.422.000.520.340.250.51.111.11.43 71.090.150.020.090.360.40.220.650.570.72 80.610.780.820.111.220.550.260.630.31-0.190.650.830.86 90.110.67-0.640.020.951.560.950.580.520.651.021.15 101.730.68-0.99-0.290.91-0.66-0.830.441.411.032.000.991.15 110.650.140.011.11-0.16-0.32-0.160.350.040.94-0.12-0.11 121.390.56-0.26-0.180.97-0.81-0.481.110.481.310.960.97 130.640.82-0.541.290.82-0.590.63 1.311.461.43 140.41.00-0.43-0.521.8-0.04-0.470.150.972.001.311.621.57 151.260.90.44-0.462.00-0.25-0.32-0.06-0.311.162.00 160.12-0.26-0.850.650.852.000.141.141.530.520.980.93 171.40.37-0.41-0.450.891.550.220.092.001.48-0.190.860.83 181.622.00-0.52 -0.830.080.410.280.010.4-0.73-0.86 Weight100.00 25.0050.00100.0065.0075.00 32.5075.00 Norm0.11 0.030.050.110.070.08 0.040.08

29. Bulgarian Academy of Sciences. 22 July, 2008 29 Construction of the synthetic indicators (  i are the normalized group weigths) 3. Determination of Indicators

30. Bulgarian Academy of Sciences. 22 July, 2008 30 Graphical representation: 3. Determination of Indicators

31. Bulgarian Academy of Sciences. 22 July, 2008 31 4. Final aggregation Let us denote by the strong and weak indicators corresponding to municipality j and to the indicator class h (h = 1, 2, 3, 4) Let us assume that the weights are assigned to the four classes of indicators

32. Bulgarian Academy of Sciences. 22 July, 2008 32 Global indicators: 4. Final aggregation Weights: –Environmental: 0.4 –Economic: 0.3 –Urban development: 0.15 –Demographic: 0.15

33. Bulgarian Academy of Sciences. 22 July, 2008 33 Graphical representation: 4. Final aggregation

34. Bulgarian Academy of Sciences. 22 July, 2008 34 Weights: two options –Give the weights ourselves and carry out a sensitivity analysis. –Determine the weights in a group decision making process like the one carried out in step 2 4. Final aggregation

35. Bulgarian Academy of Sciences. 22 July, 2008 35 Conclusions Urban indicators have been designed to measure concrete aspects of sustainability, but there is a lack of a unified measure. We have developed a full methodology to build synthetic urban indicators. Both strong and weak sustainability indicators are built and taken into account. The pair of indicators and their graphical representation allows a more in depth analysis of the data.

36. Bulgarian Academy of Sciences. 22 July, 2008 36 Conclusions The methodology developed comprises several different schemes, among which we can point out: –Meta-Goal Programming, for the determination of the group weights. –Reference point technique (objective ranking) for the construction of the indicators. The scheme can be adapted to any number of indicators and/or municipalities.

37. Bulgarian Academy of Sciences. 22 July, 2008 37 Conclusions Future Research Lines: –To carry out a wider study: Broader range (national?), higher number of municipalities. Refine the panel of experts. More reliable data. –Final aggregation: Full systematic sensitivity analysis. Classification scheme.

38. Bulgarian Academy of Sciences. 22 July, 2008 38 Conclusions Future Research Lines: –Group weights: Full group decision making process. Different penalizations for n and p. –Reference point scheme: Interval criteria. Different slopes for the branches of the achievement functions. Different aspiration and reservation values.