1 SUTRA Final Review D13 - Multi Criteria Analysis Gdansk, Poland 23 rd -24 th June 2003 Presented by the Ministry of the Environment, Israel

2 WP 13: Multi-Criteria Analysis OBJECTIVESOBJECTIVES DEVELOPMENT OF RULES AND DESCRIPTORSDEVELOPMENT OF RULES AND DESCRIPTORS MULTI CRITERIA ANALYSIS – METHODOLOGYMULTI CRITERIA ANALYSIS – METHODOLOGY MCA OPTIMISATION EXCERCISEMCA OPTIMISATION EXCERCISE RESULTSRESULTS SUTRA Final Review

3 WP 13: Multi-Criteria Analysis - Objectives The primary objectives of WP 13 “ Scenario Comparison and Multi-criteria Analysis ” is: –the comparative analysis of the set of scenarios for each city using sustainable city indicators as defined in WP 8 and 10, –the multi-criteria comparative analysis and selection of a non-dominated set of alternatives and –the identification of the most promising scenario or small set of candidate scenarios from each test site. SUTRA Final Review

4 WP 13: Multi-Criteria Analysis - Rules Based Analysis The objective of a rule-based expert system is to reduce the multidimensionality of the information and to collapse all the data into one dimension so that the different scenarios can be analysed and compared in the same terms. SUTRA Final Review

5 WP 13: Multi-Criteria Analysis Rules Based Analysis Methodology Classification of indicators (and derived indicators) into categories which define a “sustainable city” and “sustainable transportation”. SUTRA Final Review Economic Performance Social Performance Environmental Quality

6 WP 13: Multi-Criteria Analysis Rules Based Analysis Methodology SUTRA Final Review Grouping of Indicators to summarise data.

7 WP 13: Multi-Criteria Analysis Rules Based Analysis Methodology SUTRA Final Review

8 WP 13: Multi-Criteria Analysis Rules Based Analysis Methodology Derived Indicators are then developed, which aim to maximise efficiencies. Two examples are: Transportation Intensityemissions efficiency Transportation Intensity emissions efficiency Each set of derived indicator is based on a number of lower indicators. SUTRA Final Review

9 WP 13: Multi-Criteria Analysis Rules Based Analysis Methodology SUTRA Final Review

10 WP 13: Multi-Criteria Analysis Rules Based Analysis Methodology The individual indicators are qualitatively classified, to enable statistical analysis. Three ranges for each indicator is set, and standard deviation is calculated. SUTRA Final Review Example of qualitative ranges for indicator “total passenger transportation”.

11 WP 13: Multi-Criteria Analysis Rules Based Analysis Methodology Once the ranges have been established, a matrix for every primary indicator (transport intensity) is developed to show all possible combinations of alternatives. From such a matrix we can identify the combinations which represent the most efficient options and those indicators that we would want to minimise/maximise. Each combination is represented by a respective rule. TTPD = TOTAL PASSENGER TRANSPORTATION DEMAND PPDT= PUBLIC PASSENGER TRANSPORT DEMAND ADTP = AVERAGE DISTANCE TRAVELLED SUTRA Final Review

12 WP 13: Multi-Criteria Analysis Rules Based Analysis Methodology Representation of list of I}ndicators: Lisbon Example SUTRA Final Review

13 WP 13: Multi-Criteria Analysis Rules Based Analysis Methodology Development of rules and descriptors into a following structures: IF condition AND/OR condition THEN conclusion TTPD = TOTAL PASSENGER TRANSPORTATION DEMAND PPDT= PUBLIC PASSENGER TRANSPORT DEMAND ADTP = AVERAGE DISTANCE TRAVELLED SUTRA Final Review

14 WP 13: Multi-Criteria Analysis Rules Based Analysis Methodology SUTRA Final Review Descriptor Operator Value Density ==,,!=,……. high Conclusion: Descriptor Assignment Value Density = high Total transportation passenger demand A: TPTD U: [pkm/year] V: Low [ … ] V: Medium [ … ] V: High [ … ] R: 0001 / 0002 / 0003 / … Q: What is the total transportation passenger demand measured in pkm in a period of one year?

15 WP 13: Multi-Criteria Analysis Rules Based Analysis Methodology Using the rules developed, analysis of city common scenarios (as defined by FEEM) is carried out to asses the performance of each scenario. This was carried out according to the following division of indicators: transport demand, pollutant emissions, air quality, ozone concentration, stressing factors, human health, public/private transportation. SUTRA Final Review

16 WP 13: Multi-Criteria Analysis Rules Based cont…. Cross scenario comparison for transportation demand. Within the case studies, Tel Aviv is the city with the most efficient performance for scenario 3, whereas Lisbon shows the highest values for scenario 1 and 2 and Gdansk for scenarios 3 and 4. SUTRA Final Review

17 WP 13: Multi-Criteria Analysis - MCA Methodology The objective of the multi-criteria analysis is to identify within the different scenarios, the city/scenario that has the most efficient performance and which maximises the pre-defined derived indicators. The MCA also identifies the factors that leads this city to perform more efficient than the rest, for the purpose of extracting policy strategies. Optimisation is carried out via mutli criteria analysis to identify the city that performs the most efficiently and maximises pre defined indicators for transportation efficiency. SUTRA Final Review

18 WP 13: Multi-Criteria Analysis – MCA Optimisation The DSS software is used to carry out optimisation, which automatically calculates the efficient point which is closest to utopia. SUTRA Final Review

19 WP 13: Multi-Criteria Analysis – MCA Optimisation Optimisation of scenarios and cross scenario compasrions required gathering all indicator results (D12) and adding those derived indicators which we want to maximise/minimise. Each city then produces an input file per scenario. SUTRA Final Review

20 WP 13: Multi-Criteria Analysis Rules Based Analysis Methodology Representation of list of Indicators: Tel Aviv Example SUTRA Final Review

21 WP 13: Multi-Criteria Analysis Rules Based Analysis Methodology Representation of list of Indicators: Tel Aviv Example cont.. SUTRA Final Review

22 WP 13: Multi-Criteria Analysis – Results A set of indicators produced for each scenario. SUTRA Final Review

23 WP 13: Multi-Criteria Analysis – Results Definition of each indicator (range, normalisation, reference point), and location of reference point. SUTRA Final Review

24 WP 13: Multi-Criteria Analysis – Results The model allows a selection of indicators to be chosen for analysis of the optimum scenario. SUTRA Final Review

25 WP 13: Multi-Criteria Analysis – Results The efficiency point represents the best alternative given the constraints. SUTRA Final Review

26 WP 13: Multi-Criteria Analysis – Results Results of optimisation for CO2 emissions from passenger transportation vs. teleworking employment SUTRA Final Review

27 WP 13: Multi-Criteria Analysis – Results Representation of the complete set of indicator values which relate to the efficiency point/scenario. SUTRA Final Review