Fulvio Fontini Department of Economics, University of Padua (Italy) Georg Umgiesser

1. 1 The role of ambiguity in the evaluation of the net benefits of the MOSE system in the Venice lagoon Fulvio Fontini Department of Economics, University of Padua (Italy) Georg Umgiesser ISMAR-CNR, Venice (Italy) Lucia Vergano ECCET, IPTS,JRC, European Commission, Sevilla (Spain)

2. Structureof the paper 2 • Introduction to decision making under ambiguity • The Neo-additive capacities framework: introduction and graphical representation • 3. The Venice lagoon case study: analysis of the • problem and policy implications

3. Decision making under ambiguity I 3 Policy decisions impacting on the environment → Ambiguity Ecological system Scenarios whose likelihood + → can not beinferred from any Economic system probability distribution • No clear definition of the problem • → Incomplete states of the world (Mukerji, 1997) • Use of any a priori distribution not justified • (Chichilnisky, 2000)

4. Decision making under ambiguity II 4 Decision criteria: • Maximin decision criterion (Wald, 1950) - complete • ignorance (Rawls, 1971): evaluation of the worst possible • consequence - Arrow-Hurwicz criterion (1971): evaluation of a linear combination of the best and worst possible consequences → strong interpretation of the Precautionary Principle (PP):only the worst possible consequence(s)is (are)to be taken into account unless it can be proved “beyond any reasonable doubt” that some other consequences will occur

5. Capacity as a measure of ambiguity I 5 Capacity = normalized monotone measure of ambiguity ν: E → ℜ → generalisation of the subjective utility theory as a decision criterion through the Choquet Integral where f: S → X ⊂ ℜ is a utility function → the integral w.r.t. a capacity identifies the so called Choquet Expected Utility (CEU)

6. Capacity as a measure of ambiguity II 6 Wakker, 2001 Concave capacity → Overweight good outcomes → Optimistic attitude towards ambiguity Convex capacity → Overweight bad outcomes → Pessimistic attitude towards ambiguity

7. NEO-capacity as a measure of ambiguity I 7 Chateauneuf et al., 2007:NEO-additive capacity = a specific type of capacity, additive on non-extreme outcomes, reflecting pessimism for some events and optimism for some other events where: π = finitely additive probability distribution μαN (A) = Hurwicz capacity

8. NEO-capacity as a measure of ambiguity II 8 8 → The CEU calculated w.r.t. a NEO-additive capacity: where: Eπ= expected value calculated w.r.t. π f = act function

9. Special cases of the CEU 9 Several decision criteria may be interpreted as special cases of the Choquet Expected Utility framework: 1. δ=0: the Expected Value approach2. N={Ø}, δ>0, α=0: pure pessimism N={Ø}, δ=1, α=0: Maxmin criterion3. N={Ø}, δ>0, α=1: pure optimism N={Ø}, δ=1, α=1: Maxmax criterion 4. N={Ø}, δ=1, α=(0,1): Hurwicz criterion The parameters δ(1-α) and δαcapture the impact of pessimism and optimism

10. Generalization of the functional form 10 Renaming 1. λ=δ(1- α) 2. γ=δα 3. 4. the CEU function can be expressed in the following way:

11. Graphical representation I 11 the simplex constraints the set of the admissible ranges for γ and λ. The space of the CEU graphically corresponds to the side of the triangle shown in Fig. 1

12. Graphical representation II 12 Figure 1

13. Interpretation of the graphical representation I 13 1. γ=λ=0: the ExpectedValue approach2. γ=0:pure pessimism 3. λ=0:pure optimism 4. λ+γ=1:Hurwicz criterion

14. Interpretation of the graphical representation II 14 Expected Value Equivalent set: (γ,λ) s.t. CEU=Eπand the Hurwicz criterion holds → it defines the implicit values of the ambiguity attitude that a decision maker has in mind when taking the decision on the basis of the expected value only, i.e. if having an optimistic or pessimistic attitude towards it

15. The ‘acqua alta’ phenomenon in the Venice lagoon 15 ‘Acqua alta’ = the periodical high water event causing partial flooding of Venice, corresponding to +80 cm above the ‘Punta della Salute’ tidal datum During last decades: increased frequency and intensity (Fig. 2)Forecasts for the next century (IPCC, 2007: Fig. 3): further worsening of the phenomenon, due to the global sea level rise induced by climate change → Mitigation and prevention measures:hydraulic pumps, ‘vasche’, ‘paratie’, rising of pavements, MOSE

16. The ‘acqua alta’ phenomenon I 16 Figure 2

17. The ‘acqua alta’ phenomenon II 17 Figure 3

18. The ‘acqua alta’ phenomenon: the frequency 18 Figure 4 – Yearly distribution of tidal  +110 cm in Venice, 1872-2006

19. The IPCC forecasts 19 Figure 5 – Global sea level rise Sources: IPCC, 2007

20. The MOSE functioning 20 MOSE = system of mobile barriers installed on the sea floor of the inlets (‘Chioggia’, ‘Lido’ and ‘Malamocco’) → Separate from a hydraulic point of view thelagoon from the Adriatic Sea whenever the forecasted water level exceeds the safeguarding level (+100 or +110 cm above ‘Punta della Salute’)

21. The MOSE impact: benefits I 21 • avoided damages to buildings: • damages affecting the real estate stock, depending on the intensity of flooding; • 2) avoided damages to individuals: • damages to the flow of (touristic and personal) services depending on the frequency of flooding • MOSE → flooding intensity and frequency↓ →costs saved = benefits

22. The MOSE impact: benefits II 21 • avoided damages for buildings: • costs of renovation after the highest tide experienced in 2000-2002 • - plastering costs of the walls’ flooded surface • - lead plate introduction costs (historical buildings)

23. The MOSE impact: benefits I 21 2) avoided damages for individuals: costs due to displacement problems - costs of children (27% of children in schooling age) and elderly people (10% of people aged 75-84) caring - loss of touristic expenses (daily tourist flow x average daily expenses)

24. The MOSE impact: benefits II 22 Table 1 – Avoided costs and expenses for category

25. The MOSE impact: costsI 23 • → Operational and maintenance costs (11,136 thousands of €/year) • →Direct costs due to theinterferences with port activities (Tables 2-3): • additional time to getting in and out of the lagoon (charter costs) • longer period staying in a wharf/quay (mooring costs) Safeguarding level → Frequency of mobile barriers closure → Amount of the additional charter and mooring costs → The choice between a + 100 or a + 110 cm safeguarding level = example of decision under ambiguity:the likelihood of the environmental parameter (sea level rise) can not be inferred on the basis of any probability distribution

26. The MOSE impact: costs II 24 Table 2 – Charter and mooring costs for ship category

27. The MOSE impact: costs III 25 Table 3 – Charter and mooring costs for ship category

28. MOSE direct impacts economic assessment: the hydrodynamic model 26 Hydrodynamic model (Umgiesser et al., 2004): - using water level measurements at the inlets andwind over the lagoon→ simulates water levels and barotropic currents inside the lagoon→ computes how often the water level exceeds the safeguarding level and the time of mobile barriers closure - using ship traffic data for 2000-2002 (Venice harbour office) → computes how often and for how long the ship traffic is interrupted due to MOSE functioning (Umgiesser and Matticchio, 2006)

29. Direct impacts economic assessment: scenarios 27 Hydrodynamicmodel hypotheses:- Adriatic Sea level rise (0, +30 cm, +50 cm)- safeguarding level (+100 cm, +110 cm)- security increment (0, +10 cm) Table 4 – The twelve scenarios

30. Direct impacts economic assessment: the estimates 28 Table 5 – Total costs and benefits (millions of €/year) for each scenarios

31. Policy implications: decision variables 29 Environmental variable: Adriatic Sea level rise Decision making variables: safeguarding level security increment Safeguarding level → Frequency of mobile barriers closure → MOSE net benefits The choice between a safeguarding level of + 100 or + 110 cm = example of decision under ambiguity:the likelihood of the environmental parameter (sea level rise) can not be inferred on the basis of any probability distribution → choice based on the estimated costs associated to each scenario, weighted according to the decision criterion adopted by policy makers

32. Policy implications: decision criteriaI 30 No ambiguity → Expected value criterion (benchmark): states of the world are equally weighted according to a subjective probability measure pi =1/12, i=A, …, L ci, pi i=A; …,L Ambiguity → application of the CEU framework under different values of the subjective parameters

33. Policy implications: decision criteria II 31 1. Max-Min criterion (pessimistic attitude – C1) The option corresponding to the less bad outcome (B) among the worst outcomes associated with each possible decision (B,D,A,C) is taken 2. Max-Max criterion (optimistic attitude – C2) The option corresponding to the best outcome (K) among the best outcomes associated with each possible decision (K,L,I,J) is taken 3. Hurwicz criterion The best (B) and the worst (K) outcomes are equally weighted

34. Estimated net benefits: a comparison between different decision criteria 32 Table 6 – Estimated net benefits (millions of €/year)

35. Conclusions 33 • Cost estimates vary substantially according to the • attitude decision makers have towards ambiguity • (pessimism vs optimism) • 2. Even assuming a symmetric attitude of decision • makers towards ambiguity, estimated costs substantially • differ from the ones calculated following the expected • value criterion 3. Decision makers have implicitly shown pessimism, i.e. overevaluation of the scenarios providing the lowest benefits and underevaluation of those inducing the highest ones (precautionary approach)