Prof. Roberto Camagni – Politecnico di Milano General ESPON meeting Espoo, november, 2006 TEQUILA SIP ESPON 3.2 Interactive Simulation Package for Territorial Impact Assessment Roberto Camagni (Politecnico di Milano)

Prof. Roberto Camagni – Politecnico di Milano The team DIG - Department of Management, Economics and Industrial Engineering – Politecnico di Milano Roberto CAMAGNI (direction and concept) DIG – Politecnico Lidia DIAPPI (package supervision) DIAP-Politecnico Paola BOLCHI(SIP package construction) DIAP-Politecnico Chiara TRAVISI(data base and calibration) DIG - Politecnico Paolo SALZANI(data base and simulation) DIG - Politecnico

Prof. Roberto Camagni – Politecnico di Milano Content 1.The TIA / Territorial Cohesion link 2.An operational definition of Territorial Cohesion 3.Territorial dimensions and assessment criteria 4.The General Assessment Model: the TEQUILA Model 5.The Territorial Assessment Model: TIM 6.TEQUILA SIP: Interactive Simulation Package 7.Application to TENs policies 8.The interactive package 9.Mapping the results

Prof. Roberto Camagni – Politecnico di Milano 1. The TIA / Territorial Cohesion link A TIA methodology has necessarily to start by linking up with a sound theoretical and operational definition of Territorial Cohesion “Territorial cohesion translates the goal of sustainable and balanced development assigned to the Union into territorial terms” (Rotterdam Declaration, Dutch Presidency, 2004) For us: Territorial cohesion may be seen as the territorial dimension of sustainability (beyond the technological, the behavioural and the diplomatic dimensions of sustainability) (Camagni, 2004)

Prof. Roberto Camagni – Politecnico di Milano 2. An operational definition of Territorial Cohesion The 3 main components of territorial cohesion: * Territorial Efficiency: resource-efficiency with respect to energy, land and natural resources; competitiveness and attractiveness of the local territory; internal and external accessibility * Territorial Quality: the quality of the living and working environment; comparable living standards across territories; similar access to services of general interest and to knowledge * Territorial Identity: presence of “social capital”; landscape and cultural heritage; capability of developing shared visions of the future; creativity; productive “vocations” and competitive advantage of each territory

Prof. Roberto Camagni – Politecnico di Milano 2. An operational definition of Territorial Cohesion

Prof. Roberto Camagni – Politecnico di Milano 3. Territorial dimensions and assessment criteria

Prof. Roberto Camagni – Politecnico di Milano 4. The General Assessment model: the TEQUILA Model T erritorial E fficiency QU ality I dentity L ayered the TEQUILA Model A ssessment Model (Camagni, 2006)

Prof. Roberto Camagni – Politecnico di Milano 4. The General Assessment model: the TEQUILA Model 1.TEQUILA is a Multicriteria Model for the Territorial Impact Assessment of EU policies 2. The 3 components of the T.C. concept and their sub-components become the criteria in the Assessment Model 3. The weights of the 3 criteria and sub-criteria are flexible (sensitivity of results with respect to change in weights is tested interactively) 4. The general impact of EU policies on each criterion is defined using ad hoc studies, in both qualitative and quantitative ways 5. A method for combining quali-quantitative impact indicators inside the multi-criteria analysis is supplied

Prof. Roberto Camagni – Politecnico di Milano 4. The General Assessment model: the TEQUILA Model Alternative scaling of quantitative assessments (e.g.) Impact on regional employment Impact on regional employment a) “local scaling” b) “ad hoc scaling” Qualitative impact scores are attributed on a +5 to -5 scale: 5= very high advantage for all; -5= very high disadvantage for all 4= high advantage for all; -4= high disadvantage for all 3= high advantage for some, medium adv. for all; -3= high dis. for some, medium dis. for all 2= medium advantage; -2= medium disadvantage 1= low advantage; -1= low disadvantage 0= nil impact;

Prof. Roberto Camagni – Politecnico di Milano 4. The General Assessment model The 2 layers 1st layer: General Assessment of the impact of EU policies on the overall European territory: to be intended as a “potential impact” on an abstract territory (PIM) 2nd layer: “Territorial Assessment” on each region. Necessary as: -the intensity of the policy application may be different on different regions -the relevance of the different “criteria” is likely to be different for different regions, according to their utility function -the vulnerability and the receptivity of the different regions to similar “potential” impacts is likely to be different -a region may not be subject to a specific policy

Prof. Roberto Camagni – Politecnico di Milano 5. The Territorial Assessment Model: TIM TIM r = Σ c θ c. (PIM c. PI r ). S r,c. PA r TIM = territorial impact c = criterion of the multi-criteria method r = region θ c = weight of the c criterion PIM = potential impact of policy (abstract) PI = policy intensity (in region r) S r,c = sensitivity of region r to criterion c PA = policy applicability (a 0/1 variable) S r,c = D r,c. V r,c D r,c = desirability of criterion c for region r (territorial “utility function”) V r,c = vulnerability of region c to impact PIMc (receptivity for positive impacts): a vector of regional characteristics

Prof. Roberto Camagni – Politecnico di Milano 6. TEQUILA SIP: an Interactive Simulation Package The TEQUILA model is operated through an interactive simulation device, specifically built by the research team for Espon: TEQUILA SIP -interactive -easy to build and operate -working on different layers (particularly: Europe 29 and NUTS 3) As a pioneering and prototype experiment, TEQUILA SIP is applied to the assessment of the Territorial Impact of EU transport policy (TEN-TINA), using existing quantitative ESPON assessments and data base Territorial level : NUTS 3 (1329 regions) Collaboration of ESPON teams in data supply is gratefully acknowledged

Prof. Roberto Camagni – Politecnico di Milano 7. Application to TENs policies 3 criteriaVariables9 sub-criteria PIM_E1Internal connectivity Territorial EfficiencyPIM_E2External Accessibility PIM_E3Economic Growth PIM_Q1Congestion Territorial QualityPIM_Q2Emissions PIM_Q3Transport sustainability PIM_I1Creativity Territorial IdentityPIM_I2Cultural heritage PIM_I3Landscape resources

Prof. Roberto Camagni – Politecnico di Milano 7. Application to TENs policies : Potential Impact PIM Sub-criteriaIndicatorUnit of measureDir.VariationWgt.Source of data PIM_E1 Internal Connectivity Dif transport endowment (road + rail)/GDP Km / GDP+0 to 40,333 ESPON 3.2 Mcrit PIM_E2 External Accessibility Dif accessibility (road/rail passenger travel), scenario B1 (only priority projects) Number of people+2 to 50,333 ESPON 1,2,1 SASI; Mcrit PIM_E3Growth Dif GDP per capita, scenario B1 – Difference to reference scenario 2000 – 2021 Dif % GDP/inhabitant+2 to 40,333 ESPON 2,1,1, SASI Model PIM_Q1CongestionDif-flows, baseline scenario 2015Million Vehicles/Km- 2 to -50,333 ESPON 3.2 Mcrit PIM_Q2EmissionsDif CO2 emissions baselineMillion Tons CO2 / Year- 2 to -50,333 ESPON 3.2 Mcrit PIM_Q3 Transport sustainability Dif rail - Dif road, baseline scenario Km - Km+-3 to 30,333 ESPON 3.2 Mcrit PIM_I1Creativity Dif accessibility*[knowledge and creative services] (# people)*( # libraries + theatres) +1 to 40,333 ESPON 2,1,1, SASI Model PIM_I2Cultural heritage Dif accessibility*[ # monuments + museums ] (# people)*( # monuments- museums) +1 to 40,333 ESPON 2,1,1, SASI Model PIM_I3Landscape Dif. Transport endowment (road+rail) / GDP Km / GDP-0 to -40,333 ESPON 3.2 Mcrit

Prof. Roberto Camagni – Politecnico di Milano 7. Application to TENs policies : Sensitivity SensitivitySensitivity parametersUnit of measureVariationFunctional shape Source of data S_E1 D = LOG of current density of transport endowment [density=(road+rail)/GDP] R = 1 S = D norm LOG[km road+rail] / GDP0,8 to 1,2Linear ESPON 3.2 Mcrit ESPON 3.1 S_E2 D = LOG [current accessibility] R = 1 S = D norm LOG [# of people daily accessible by car] 0,8 to 1,2Non Linear ESPON 2,1,1 – SASI Model S_E3 D = GDP 2000 PPP per inhabitant R = 1 S = D norm GDP 2000 PPP per inhabitant]0,9 to 1,2Linear ESPON 3.1, Eurostat Regio S_Q1 D=Present congestion V=Share of natural areas S= mean of normalised D and V D= Million Vehicles / network Km V= share of natural areas (Km 2 ) 0,8 to 1,2D = Non Linear ESPON 3.2 – Mcrit; BBR Corine Landcover S_Q2 D=Present emissions V=Share of natural areas S= mean of normalised D and V Present emissions CO2 year 2000 [million tons] V= share of natural areas (Km 2 ) 0,8 to 1,2 0,9 to 1,2 D = Non Linear V = Linear ESPON Mcrit BBR Corine Landcover S_Q3 D=Present share of railways on total tran. ntw. R = 1 S = D norm Km / Km (%)0,8 to 1,2D = Non Linear ESPON 3.2 Mcrit S_I1 D=GDP 2000 PPP per inhabitant R = 1 S = D norm GDP 2000 PPP per inhabitant0,9 to 1,2Linear ESPON 3.1, Eurostat Regio S_I2 D=GDP 2000 PPP per inhabitant R = 1 S = D norm GDP 2000 PPP per inhabitant0,9 to 1,2Linear ESPON 3.1, Eurostat Regio S_I3 D=1 V = Natural vulnerability (natural area fragmentation) S= V norm Natural area fragmentation indicator 1-5: 1= very low; 5 = max fragmentation 1,2 to 0,9Linear ESPON 1,3,1; GTK

Prof. Roberto Camagni – Politecnico di Milano 8. The interactive package

Prof. Roberto Camagni – Politecnico di Milano 8. The interactive package

Prof. Roberto Camagni – Politecnico di Milano 8. The interactive package: Impact on Efficiency

Prof. Roberto Camagni – Politecnico di Milano 8. The interactive package: Potential Impact

Prof. Roberto Camagni – Politecnico di Milano 9. Mapping results: impact on Territorial Efficiency Politecnico di Milano – TEQUILA SIP – June 2006 Mean Value =

Prof. Roberto Camagni – Politecnico di Milano 9. Mapping results: impact on Territorial Quality Politecnico di Milano – TEQUILA SIP – June 2006 Mean Value = -1.19

Prof. Roberto Camagni – Politecnico di Milano 9. Mapping results: impact on Territorial Identity Politecnico di Milano – TEQUILA SIP – June 2006 Mean Value = 0.714

Prof. Roberto Camagni – Politecnico di Milano 8. Mapping results: General Impact (a) Politecnico di Milano – TEQUILA SIP – June 2006 Mean Value =

Prof. Roberto Camagni – Politecnico di Milano 8. Mapping results: General Impact (b) Politecnico di Milano – TEQUILA SIP – June 2006 Mean Values =

Prof. Roberto Camagni – Politecnico di Milano Thanks! Thanks for your attention! Roberto Camagni Department of Management, Economics and Industrial Engineering Politecnico di Milano Piazza Leonardo da Vinci MILANO tel: secr. fax: