General ESPON meeting Espoo, 14-15 november, 2006
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Transcript of General ESPON meeting Espoo, 14-15 november, 2006
Prof. Roberto Camagni – Politecnico di Milano
General ESPON meeting General ESPON meeting Espoo, 14-15 november, 2006Espoo, 14-15 november, 2006
TEQUILA SIP ESPON 3.2Interactive Simulation Package
for Territorial Impact Assessment
Roberto Camagni (Politecnico di Milano)
Prof. Roberto Camagni – Politecnico di Milano
The team 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 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
3. Territorial dimensions and assessment criteria3. Territorial dimensions and assessment criteria
Ec
Soc
Territorial quality
Territorial efficiency
Territorial identity
Env
Quality of life and working conditions; access to services of general interest
Resource -efficiency Competitiveness, attractiveness
Social capital; shared visions
Sustainable transport: s hare of public transport and reduction of congestion on the network
Compact city form; reduction of sprawl
Co -operation between city and countryside
Integrated and balanced territori al system
Efficient and polycentric urban system
Inter -regional integration
Complementarity of knowledge and Know -how
Multiethnic solidarity and integration
Conservation and creative management of cultural resources
Conservation and creative management of natural landscape
Reduction of poverty and exclusion
Economic performance
Employment performance
Accessibility to telecommunication services and to knowledge
Strengthening of gateway cities
Financial costs and benefits of
policies
Conservation of natural resources Conservation of water resources
Accessibility to infrastructure
Reduction of environmental risks
Quality of services
Quality of transport services
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 ModelA 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.)
+5
0
180 250 180 250 Impact on regional employment Impact on regional employment
+3
+2
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 all4= high advantage for all; -4= high disadvantage for all3= high advantage for some, medium adv. for all; -3= high dis. for some, medium dis. for all2= medium advantage; -2= medium disadvantage1= 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: TIM5. The Territorial Assessment Model: TIM
TIMr = Σc θc . (PIMc . PIr ) . Sr,c . PAr
TIM = territorial impactc = criterion of the multi-criteria methodr = regionθc = weight of the c criterionPIM = potential impact of policy (abstract)PI = policy intensity (in region r)Sr,c = sensitivity of region r to criterion cPA = policy applicability (a 0/1 variable)
Sr,c = Dr,c . Vr,cDr,c = desirability of criterion c for region r (territorial “utility function”)Vr,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 Package6. 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 policies7. Application to TENs policies
3 criteria Variables 9 sub-criteria
PIM_E1 Internal connectivity
Territorial Efficiency PIM_E2 External Accessibility
PIM_E3 Economic Growth
PIM_Q1 Congestion
Territorial Quality PIM_Q2 Emissions
PIM_Q3 Transport sustainability
PIM_I1 Creativity
Territorial Identity PIM_I2 Cultural heritage
PIM_I3 Landscape resources
Prof. Roberto Camagni – Politecnico di Milano
7. Application to TENs policies 7. Application to TENs policies : Potential Impact: Potential Impact
PIM Sub-criteria Indicator Unit of measure Dir. Variation Wgt. Source of data
PIM_E1InternalConnectivity
Dif transport endowment (road + rail)/GDP
Km / GDP + 0 to 4 0,333ESPON 3.2Mcrit
PIM_E2ExternalAccessibility
Dif accessibility (road/rail passenger travel), scenario B1 (only priority projects)
Number of people + 2 to 5 0,333ESPON 1,2,1 SASI; Mcrit
PIM_E3 GrowthDif GDP per capita, scenario B1 –
Difference to reference scenario 2000 – 2021
Dif % GDP/inhabitant + 2 to 4 0,333ESPON 2,1,1, SASI Model
PIM_Q1 Congestion Dif-flows, baseline scenario 2015 Million Vehicles/Km - 2 to -5 0,333ESPON 3.2Mcrit
PIM_Q2 Emissions Dif CO2 emissions baseline Million Tons CO2 / Year - 2 to -5 0,333ESPON 3.2Mcrit
PIM_Q3Transport sustainability
Dif rail - Dif road, baseline scenario 2000-2015
Km - Km + -3 to 3 0,333ESPON 3.2Mcrit
PIM_I1 CreativityDif accessibility*[knowledge and
creative services](# people)*( # libraries +
theatres)+ 1 to 4 0,333
ESPON 2,1,1, SASI Model
PIM_I2 Cultural heritageDif accessibility*[ # monuments +
museums ](# people)*( # monuments-
museums)+ 1 to 4 0,333
ESPON 2,1,1, SASI Model
PIM_I3 LandscapeDif. Transport endowment
(road+rail) / GDP Km / GDP - 0 to -4 0,333
ESPON 3.2Mcrit
Prof. Roberto Camagni – Politecnico di Milano
7. Application to TENs policies 7. Application to TENs policies : Sensitivity: Sensitivity
Sensitivity Sensitivity parameters Unit of measure Variation Functional shape
Source of data
S_E1
D = LOG of current density of transport endowment [density=(road+rail)/GDP]
R = 1S = D norm
LOG[km road+rail] / GDP 0,8 to 1,2 Linear ESPON 3.2McritESPON 3.1
S_E2D = LOG [current accessibility]R = 1S = D norm
LOG [# of people daily accessible by car]
0,8 to 1,2 Non Linear ESPON 2,1,1 – SASI Model
S_E3D = GDP 2000 PPP per inhabitantR = 1S = D norm
GDP 2000 PPP per inhabitant] 0,9 to 1,2 Linear ESPON 3.1, Eurostat Regio
S_Q1D=Present congestion V=Share of natural areasS= mean of normalised D and V
D= Million Vehicles / network KmV= share of natural areas (Km2)
0,8 to 1,2 D = Non Linear ESPON 3.2 –Mcrit; BBR Corine Landcover
S_Q2D=Present emissionsV=Share of natural areas S= mean of normalised D and V
Present emissions CO2 year 2000 [million tons]
V= share of natural areas (Km2)
0,8 to 1,20,9 to 1,2
D = Non LinearV = Linear
ESPON 3.2 -McritBBR Corine Landcover
S_Q3D=Present share of railways on total tran. ntw.R = 1S = D norm
Km / Km (%) 0,8 to 1,2 D = Non Linear ESPON 3.2Mcrit
S_I1D=GDP 2000 PPP per inhabitantR = 1S = D norm
GDP 2000 PPP per inhabitant 0,9 to 1,2 Linear ESPON 3.1, Eurostat Regio
S_I2D=GDP 2000 PPP per inhabitantR = 1S = D norm
GDP 2000 PPP per inhabitant 0,9 to 1,2 Linear ESPON 3.1, Eurostat Regio
S_I3
D=1V = Natural vulnerability (natural area
fragmentation)S= V norm
Natural area fragmentation indicator 1-5: 1= very low; 5 = max fragmentation
1,2 to 0,9 LinearESPON 1,3,1; GTK
Prof. Roberto Camagni – Politecnico di Milano
8. The interactive package: Impact on Efficiency8. The interactive package: Impact on Efficiency
Prof. Roberto Camagni – Politecnico di Milano
8. The interactive package: Potential Impact 8. The interactive package: Potential Impact
Prof. Roberto Camagni – Politecnico di Milano
9. Mapping results: impact on Territorial Efficiency 9. Mapping results: impact on Territorial Efficiency
Politecnico di Milano – TEQUILA SIP – June 2006
Mean Value = 2.1773
Prof. Roberto Camagni – Politecnico di Milano
9. Mapping results: impact on Territorial Quality9. 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 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)8. Mapping results: General Impact (a)
Politecnico di Milano – TEQUILA SIP – June 2006
Mean Value = 0.5492
Prof. Roberto Camagni – Politecnico di Milano
8. Mapping results: General Impact (b)8. Mapping results: General Impact (b)
Politecnico di Milano – TEQUILA SIP – June 2006
Mean Values = 0.5173
Prof. Roberto Camagni – Politecnico di Milano
Thanks!Thanks!
Thanks for your attention!
Roberto CamagniDepartment of Management, Economics and Industrial EngineeringPolitecnico di MilanoPiazza Leonardo da Vinci 32 - 20133 MILANOtel: +39 02 2399.2744 - 2750 secr.fax: +39 02 [email protected]://econreg.altervista.org