Territorial Impact Assessment for European regions: A methodological proposal and an application to EU transport policy

Evaluation and Program Planning 32 (2009) 342–350

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Territorial Impact Assessment for European regions: A methodological proposal and an application to EU transport policy Roberto Camagni * Department of Management, Economics and Industrial Engineering, Piazza Leonardo da Vinci 32, 20133 Milano, Italy

A R T I C L E I N F O

A B S T R A C T

The need to engage European research and institutions in the new field of Territorial Impact Assessment, from both a methodological and a procedural perspective, was stated some years ago by the European Spatial Development Perspective (ESDP). The necessity of multidimensional evaluation of the likely impact of policies and programmes on the territory – understood as the dimension on which all the other relevant dimensions (economic, social, environmental and cultural) converge and with which they integrate – emerged as a natural consequence of the importance of spatial aspects in the future development of the Union and of widespread preoccupations about certain emerging spatial trends. A proposal for a TIA methodology combining logical consistency vis-a`-vis the Union’s present institutional and policy guidelines with operational viability is being developed and applied to TransEuropean Networks policy of the EU. Territorial impact is linked to an innovative definition of the objective of ‘‘territorial cohesion’’ of the Treaties in terms of territorial efficiency, quality and identity. Utilising sectoral impact studies developed inside the ESPON programme and developing territorial indicators for impact, vulnerability and desirability (territorial utility functions), a multicriteria model (TEQUILA) is implemented on priority projects as defined by the Commission, and results mapped and described for the 1360 NUTS-3 regions of the Union. ß 2009 Elsevier Ltd. All rights reserved.

1. Introduction In this paper, a model of Territorial Impact Assessment is presented, to be used in the evaluation of the effects of the policies of the European Union on regions. The model is intended to be at the same time theoretically sound, simple and operational, and it is applied to the impact assessment of the European transport policy – TEN-TINA: Trans-European Networks priority projects as indicated by the Transport Infrastructure Needs Assessment process – on NUTS-3 level (namely European provinces and similar sub-regional areas). The need to engage European research and institutions in the new field of Territorial Impact Assessment (TIA), both form a methodological and a procedural perspective, was launched some years ago by the European Spatial Development Perspective (ESDP) (CMSP, 1999). The necessity of a multidimensional evaluation of the likely impact of policies and programmes on the territory – intended as the dimension on which all the other relevant dimensions converge and integrate, namely the economic, the social, the environmental and the cultural one – emerged as a

* Tel.: +39 02 23 99 2744; fax: +39 02 2399 2710. E-mail address: [email protected]. 0149-7189/$ – see front matter ß 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.evalprogplan.2009.06.014

natural consequence of the relevance of spatial aspects in the future development of the Union and of widespread preoccupations about some emerging spatial trends. This indication was subsequently strengthened by the Commission’s proposal to include ‘‘territorial cohesion’’ (TC) as a major objective of regional policies (CEC, 2004a) and, a fortiori, by the inclusion of territorial cohesion among the main aims of the Union, together with economic and social cohesion, in the new Treaty approved by the European Ministers in October 2007. As a consequence, the mission of developing an operational approach to TIA was assigned by the Commission to the ESPON Programme, the European Spatial Planning Observation Network, and included in the terms of reference of many ESPON projects. A proposal for a TIA methodology, coping logical consistency with respect to the present institutional and policy guidelines of the Union and operational viability, was developed and applied by this author inside ESPON project 3.2, dealing with ‘‘Spatial scenarios and orientations in relation to the ESDP and cohesion policy’’1; its main features and results are presented here. The main ideas refer to: (a) the necessity to link a methodology for Territorial

1 The general features of the proposed approach were presented by the author at the ESPON Workshop in Evora, November 2007.

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Impact Assessment to a sound concept of ‘‘territorial cohesion’’; (b) to calculate a full territorial impact merging a ‘‘potential’’ impact (on different territorial dimensions: economy, environment, landscape, society, etc.) with a regional ‘‘sensitivity’’ indicator to each impact; and (c) to devise a multicriteria methodology integrating quantitative and qualitative assessments at different territorial levels. Section 2 presents the institutional engagement on TIA; Section 3 presents the official and a new operational definition of territorial cohesion, on which the assessment model is based; Section 4 presents the theoretical model in its methodological details and Section 5 its application to the case of priority projects in EU transport policy. Some conclusions on the viability of the entire methodology are drafted in Section 6. 2. Territorial Impact Assessment: the institutional engagement The necessity of developing a consistent methodology for TIA was taken up by the European Spatial Development Perspective (ESDP) (CMSP, 1999). The sphere of transport policies was indicated as a priority one, confronted with an accessibility/ environment trade-off but also with the challenge of a spatially equilibrated infrastructure endowment and provision: ‘‘Comprehensive integrated spatial development strategies’’ are needed, and ‘‘in the future, Territorial Impact Assessment should be the basic prerequisite for all large transport projects’’ (par. 109).2 The engagement to develop a coherent methodology for TIA was subsequently emphasized at the Informal Ministerial Meeting in Tampere, September 1999, with the adoption of the ESDP Action Programme: ‘‘The development of a common concept for Territorial Impact Assessment (TIA) is necessary to support spatial development policies. The concept shall be of a cross-sectoral nature and include socio-economic, environmental and cultural indicators for the territory in question’’.3 3. Territorial cohesion: the official view and a new proposal The proposal of ‘‘territorial cohesion’’ as a major objective of the Union was made by the Commission in February 2004 (CEC, 2004a), and authoritatively relaunched by the draft Constitution of the Union (June 2004) and the new Treaty (October 2007): ‘‘The Union . . . shall promote economic, social and territorial cohesion. . .’’ (art. 3.3 of the new Treaty on the European Union).4 Unfortunately, the very concept of territorial cohesion, distinct from the traditional socio-economic cohesion, still remains somehow fuzzy and deserves clarification and logical consistency (Faludi, 2007). In the Third Cohesion Report the Commission refers to it as a synonym for ‘‘more balanced development’’, for ‘‘territorial balance’’ or ‘‘avoiding territorial imbalances’’ (CEC, 2004a, p. 27), elements that do not add much in definitional terms with respect 2 Other spheres are natural resource management (par. 138) and water resource management (par. 145); TIA is also recommended explicitly in the policy options paragraphs (policy options no. 29, 42, 52). 3 Along similar lines, in 2002 the Commission introduced a new Impact Assessment (IA) procedure, designed to contribute to a more coherent implementation of the Sustainable Development Strategy through the assessment of the potential impact of all policy options and proposals (CEC, 2002, 2005) integrating different dimensions (economic, environmental and social) and replacing all previous single-sector type assessments (environmental, gender, business, health assessments) (CEC, 2004b). The general goal is similar to the TIA one; the main difference regards the aggregate territorial perspective of IA, as its main level is a comprehensive, Europe-wide one (CEC, 2004b, p. 11), while TIA should apply both to the general and the specific territorial level. 4 The importance of this concept being included is strengthened by the reference that in the area of territorial cohesion, the Union has a ‘shared competence’ with Member States (art. 4c of the Treaty on the Functioning of the European Union).

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to the traditional aim of socio-economic cohesion. More telling is the subsequent specification of the aspects that the new concept encompasses, at the different territorial levels: the excessive concentration of economic activity and population in the European ‘‘pentagon’’, the imbalance between the main metropolitan areas and the rest of the countries, the growing congestion and pollution and the persistence of social exclusion in the main conurbations, the presence of rural areas suffering from inadequate economic links and peripherality, the sprawling nature of urban growth, the accumulation of natural and geographical handicaps in outermost areas. More thoroughly, a subsequent report (CEC, 2004c) indicates that territorial cohesion ‘‘translates the goal of sustainable and balanced development assigned to the Union into territorial terms’’ (CEC, 2004c, p. 3), a definition which was relaunched by the Dutch Presidency conclusions of the Informal Ministerial Meeting in Rotterdam (Dutch Presidency, 2004) and by the Luxembourg Presidency in 2005: ‘‘In practical terms territorial cohesion implies: focusing regional and national territorial development policies on better exploiting regional potentials and territorial capital – Europe’s territorial and cultural diversity; better positioning of regions in Europe . . .facilitating their connectivity and territorial integration; and promoting the coherence of EU policies with a territorial impact. . .’’ (p. I; emphasis in the text) (Luxembourg Presidency, 2005). It is this author’s opinion that, if the concept of territorial cohesion has to add to the content of economic and social cohesion, it must necessarily link with the sustainability issue. In a word, territorial cohesion may be seen as the territorial dimension of sustainability. Similarly to the concept of sustainability, it bears at the same time a positive and a normative sense (i.e., it defines a condition and a policy goal) and operates by integrating different dimensions: the economic, the social and the environmental one (Camagni, 2006). The preceding definition may be explained in the following way. Considering both the positive and the normative side, sustainability conditions and goals refer to four main (policy) dimensions (Camagni, 1998): the technological dimension, governing production processes; the behavioral dimension, determining life-styles, consumption habits and also organizational models of production (e.g. transport intensive models like just-in-time); the diplomatic dimension, referring to the international strategies to assure co-operation among countries at different development levels, with different development expectations; and the territorial dimension, residing in an ordered, resource-efficient and environmental-friendly spatial distribution of human activities. In our opinion, territorial cohesion refers directly to the last dimension. Taking this reflection further, we can envisage three main components/objectives of territorial cohesion, namely:  Territorial efficiency: resource-efficiency with respect to energy, land and natural resources; competitiveness of the economic fabric 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’’; capability of developing shared visions of the future; local know-how and specificities, productive ‘‘vocations’’ and competitive advantage of each territory. While the first two objectives are rather familiar, the third, namely territorial identity, may be seen as rather surprising, but is in our opinion crucial and will become increasingly central for European policies. In fact, territorial identities incorporated in local

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culture, know-how, social capital and landscape are the basic constituents of the territorial realm as, at the same time:  they represent the ultimate glue of local societies,  are linked with the spatial division of labor and in many cases determine its evolution,  facilitate processes of collective learning and consequently boost the efficiency of the local production fabric (Camagni, 2002).5 The three dimensions of territorial efficiency, quality and identity, in their positive sense, constitute the pillars of territorial cohesion; on the other hand, in their normative sense, they may constitute the criteria on which a TIA methodology may be built (Camagni, 2006). 4. The TEQUILA model: a proposal for a TIA methodology Starting from the previous reflections, an operational model for Territorial Impact Assessment is proposed, with the following goals and characteristics: (i) A TIA methodology should allow an integrated assessment of the territorial effects of policies, programmes and large integrated projects at different spatial levels; in particular at the general EU level and at the regional level. (ii) A sound TIA methodology has necessarily to link up with the concept of territorial cohesion. The theoretical definition of TC and its three main dimensions represent the basic elements on which the assessment methodology is built: Territorial Efficiency Quality Identity Layered Assessment (TEQUILA) Model. (iii) TEQUILA is a Multicriteria Model; given the multiplicity of the ‘‘dimensions’’ of territory, this well-known assessment approach looks the most appropriate (Beinot & Nijkamp, 2007; Nijkamp, Rietveld, & Voogd, 1990) and is widely followed in a similar practice, namely in strategic environmental assessment (Nardini, 1997; The´rivel & Partidario, 1996). The three dimensions of the TC concept and their subcomponents become the criteria and sub-criteria in the assessment model. (iv) The weights of the three criteria and sub-criteria may be defined in a multiple and flexible way: through an internal expert discussion; through a discussion with policy makers; through Delphi inquiries; or else. In principle, they should not vary with respect to the policies analysed. Inside single assessment experiments, sensitivity of results with respect to change in weights should be tested. (v) The general impact of each EU policy on each dimension/ criterion have to be defined using ad hoc studies (quantitative assessment) and/or expert judgements (qualitative assessment). Cause/effect relations should be carefully inspected; (vi) The method accomodates in consistent and statistically sound ways both qualitative and quantitative impacts (see point (xi)). Qualitative impact scores are attributed to a 5 to +5 scale, easily referable to the usual 1 to +1 scale.6 (vii) The TEQUILA model may supply a first, mainly qualitative, General Assessment (GA) of the impact of EU policies on the 5 In the Scoping document of the Luxembourg Presidency (2005), natural but also cultural values are indicated as part of the endogenous potential of the different areas, worth a full exploitation. It is worth mentioning that the ESDP (CMSP, 1999) begins and ends with a reference to culture, cultural variety and cultural heritage as a characteristic feature of the European territorial identity. 6 5 = very high advantage for all; 4 = high advantage for all; 3 = high advantage for some, medium advantage for all; 2 = medium advantage; 1 = low advantage; 0 = nil impact; 1 = low disadvantage; 2 = medium disadvantage; 3 = high disadvantage for some, medium disadvantage for all; 4 high disadvantage for all; 5 = very high disadvantage for all.

overall European territory (1st layer). This assessment may be performed for each of the three dimensions (territorial efficiency, quality, and identity); then an overall GA could be performed, provided that the relative weights of the three main dimensions/criteria are defined. (viii) The preceding ‘‘general’’ assessment has to be made truly ‘‘territorial’’, considering the specificities of the single European regions, as: (a) the intensity of the policy application may be different on different regions, (b) the impact could be diversified according to regional specificities, (c) the relevance of the different ‘‘criteria’’ of the assessment method is likely to be different for different regions (e.g.: the same increase in income has a different relevance according to the development level already achieved by the single regions), (d) the vulnerability to the same potential impact may be different in different regions (e.g.: the same potential risk of a nuclear plant has a different impact on a low-density vs. high density region). (ix) Consequently, a Territorial Impact model (TIM) is built, for assessing the impact on single regions r (2nd layer). It is intended to be simple, operational and relatively userfriendly: X TIMr ¼ uc ðSr;c PIMr;c Þ c

where TIM is the territorial impact (for each dimension: efficiency, quality, identity), c the criterion and sub-criterion of the multicriteria method, r the region, uc the weight of the c P criterion/sub-criterion (0  uc  1; cuc = 1), Sr,c the sensitivity of region r to criterion c, and PIMr,c is the potential impact of policy (abstract) on criterion c according to quantitative assessment. (x) The rationale for the previous equation is the following: similarly to what is done in risk assessment, where risk = hazard (potential risk)  vulnerability, here the territorial impact is seen as the product of a potential impact (PIM) times a sensitivity indicator. On its turn, Sr,c is a vector (weighted sum) of regional characteristics, defining two main elements: vulnerability to impact (mainly geographic indicators) and preference/desirability of the dimension/criterion (technically: the territorial ‘‘utility function’’, with socioeconomic indicators) of region r: Sr;c ¼ Dr;c V r;c where Dr,c is the desirability of criterion c for region r (territorial ‘‘utility function’’) and Vr,c is the vulnerability of region r to impact on c (receptivity for positive impacts). (xi) The potential impact PIM is calculated through appropriate quantitative models defining impacts on each criterion c and each region r. As quantitative impacts are defined in their own specific measurement units and scales, they are translated and normalised into a value score on the +5/5 scale (mentioned at point (vi)). Two different methods may be utilised: assigning to the +5/5 (or 5/0) scale respectively the minimum and maximum expected or likely values (‘‘global scaling’’) or the minimum and maximum values currently obtained (‘‘local scaling’’). A third method is suggested here, that we could call ‘‘ad hoc scaling’’, more consistent in the present statistical framework: assigning the current values of the regional impacts to an interval defined inside the abstract +5/5 scale according to a subjective judgement on the absolute relevance of the impacts assessed. In fact, these

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Fig. 1. Alternative scaling procedures in quantitative assessment. (a) Local scaling and (b) ad hoc scaling.

impacts on the single regions could well be similar and belong to a small ‘‘qualitative’’ interval (say: from 180 to 250 new jobs created), and it would not be wise to assign them to a scale going from an absolute minimum to an absolute maximum (+5/0) but only to a sub-interval (+3/+2) (Fig. 1).7 (xii) In case a quantitative assessment of the PIMr,c is not available, a qualitative impact assessment could be reached through various expert judgement methodologies. In this case the PIMr,c term is substituted for by the following expression: ðPIMc PIr Þ where PIMc is the abstract general impact of policy on the c criterion (general assessment, defined on the +5/5 scale), and PIr is the policy intensity in region r. Therefore, in the qualitative assessment case the general model for TIM becomes: X uc Sr;c ðPIMc PIr Þ TIMr ¼ c

(xiii) Regarding sub-criteria inside each of the three main dimensions/criteria of TC, they may be listed tentatively in the following: (a) Territorial efficiency: efficient and polycentric urban system (*); inter-regional integration (*); resource efficiency: consumption of energy, land, water, etc.; general accessibility; infrastructure endowment; competitiveness of production system; sustainable transport: share of public transport and absence of congestion; development of city-networks and medium size cities; compact city form, reduction of sprawl; reduction of technological and environmental risk. (b) Territorial quality: reduction of interregional income disparities (*); conservation and creative management of natural resources; access to services of general interest; quality of life and working conditions; quality of transport and communication services, safety; reduction of emissions; attractiveness for external firms; reduction of poverty and exclusion; multiethnic solidarity and integration; employment performance. (c) Territorial identity: conservation and creative management of cultural heritage; quality of urban and rural landscapes; cooperation between city and coutryside; development of region-specific know-how and knowledge; accessibility to global knowledge and creative ‘‘blending’’ with local knowledge; development of territorial ‘‘vocations’’ and ‘‘visions’’; development of social capital, shared behavioural rules. Some of them are activated only when some kinds of policy proposals are considered; some others are activated only in the 7 ‘‘Ad hoc’’ scaling is conceptually similar to ‘‘global scaling’’, but much easier to handle in operational terms, as it avoids the necessity of exactly defining the minimum and maximum possible values of the impact.

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General Assessment model and not in the territorial one, as they refer to interregional conditions (integration, disparities, etc.) (with a * in the list). The list of criteria/sub-criteria should be carefully inspected, in order to control for completeness, independence and double counting. (xiv) The methodology for aggregating impacts on different criteria and regions is highly simplified: is based on Weighted Summation and has its roots in Multiattribute Utility Theory (OECD, 2005; Radej, 2008; Scriven, 1994; Eggenberger and Partidario, 2000). In its present form, in spite of its full transparency, it leaves wide areas of subjectivity and potential lack of robustness. These areas refer to: (a) the definition of the weights of the different criteria (value function of the decision maker), (b) the definition of the intervals inside the +5/5 scale (for PIMs), (c) the definition of the vulnerability indicators and their scaling, (d) the definition of the desirability indicators and the form of the territorial utility function, which generates changes on the general weighting system according to the specificities of the single regions (the same impact in terms of GDP increase may be differently appreciated by a rich and a poor region). In all these cases, a more thorough implementation of the model should imply appropriate methodologies to test for the robustness of the proposed weighting system (the swing method, pairwise comparison, trade-off method and qualitative translation, absolute and relative rankings, holistic scaling) (Jacquet-Lagre`ze & Siskos, 1982),8 and the form of the territorial utility functions. (xv) Weights are at the same time ‘‘importance’’ coefficients and ‘‘substitution indicators’’ (marginal substitution rates among indicators, allowing compensations among different impacts). Therefore, ‘‘summative’’ evaluations as those that are proposed here implicitly imply compensation among the different impacts. But also non-compensatory aggregation methods (like the one proposed by Munda (1995), with the noncompensatory multicriteria approach) or partially non-compensatory methods like the one defined by Nijkamp and Ouwersloot (1997), with the ‘‘Flag’’ model (non-compensation beyond a certain threshold of the impacts) might be explored.9 In synthesis: the TEQUILA model makes use of a well established methodology, namely multicriteria analysis, and applies it in a simplified but careful and creative way to a new scientific mission, namely building a theoretically sound and operational methodology for Territorial Impact Assessment. In this field, it should be considered as a pioneering model, as no other attempts have been carried out insofar.10 5. TEQUILA SIP: an Integrated Simulation Package for TIA The TEQUILA model is implemented through an Interactive Simulation Package, the SIP. It is conceived and built according to 8 We are not interested here in the ranking of impacts/scores on different regions (attributed by the specific assessment models) but in the relative ‘‘importance’’ of the different impacts. 9 Concerning substitution rates, we prefer additive/linear aggregation methods where substitution rates are constant, because of their simplicity; non-linearities can be better included in the model through the value functions, the vulnerability functions and the scaling procedures. 10 An important scoping document on the subject was produced by Williams, Connolly, and Healey (2000); subsequent reflections have been realised within the ESPON 2006 Programme, mainly on requirements of a TIA methodology or on partial, non-regionalized, experiences (see ESPON 3.1 and 3.2 projects on the espon.eu website).

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Map 1. Priority TEN-TINA projects defined by the EU Commission in 2003.

some specific characteristics, which were explicitly requested by the ESPON Committee. In fact it is due to be:

 potentially useful for the assessment of different European policies.

 interactive, and therefore directly usable during a public presentation and discussion;  easy to build and to apply;  transparent in its basic assumptions and in the presentation of instrumental variables;  flexible in the definition of basic parameters and in the form of the algebraic functions;  working on different territorial levels (EU-28 and NUTS-2/3);

As a pioneering experiment, the model is applied to the Territorial Impact Assessment of European transport policy, in particular the priority links in the TEN-TINA networks, using existing quantitative sectorial evaluations and database worked out inside the ESPON Programme. The chosen territorial level is NUTS-3 (provinces), encompassing 1.360 observation units in the ESPON space (28 countries, with Norway and Switzerland). Kind cooperation with other ESPON teams in data supply is acknowledged.

Table 1 Quantitative impact variables (PIMr,c). PIMr

Sub-criteria

Indicator

Unit of measure

Dir.

Variation

Weight

Source of data

PIM_E1

Internal connectivity External accessibility Growth

Dif transport endowment (road + rail)/GDP

km/GDP

+

0–4

0.333

ESPON 3.2

Dif accessibility (road/rail passenger travel), scenario B1 (only priority projects) Dif GDP per capita, scenario B1 – difference to reference scenario 2000–2021 Dif-flows, baseline scenario 2015 Dif CO2 emissions baseline Dif rail–Dif road, baseline scenario 2000–2015

Number of people

+

2–5

0.333

Dif % GDP/inhabitant

+

2–4

0.333

Million vehicles/km Million tons CO2/year km–km

  +

2 to 5 2 to 5 3 to 3

0.333 0.333 0.333

ESPON 1,2,1, SASI; Mcrit ESPON 2,1,1, SASI Model ESPON 3.2 Mcrit ESPON 3.2 Mcrit ESPON 3.2 Mcrit

Dif accessibility  [knowledge and creative services]

(# people)  (# libraries + theatres) (# people)  (# monuments  museums) km/GDP

+

1–4

0.333

+

1–4

0.333



0 to 4

0.333

PIM_E2 PIM_E3 PIM_Q1 PIM_Q2 PIM_Q3 PIM_I1

Congestion Emissions Transport sustainability Creativity

PIM_I2

Cultural heritage

Dif accessibility  [# monuments + museums]

PIM_I3

Landscape

Dif. Transport endowment (road + rail)/GDP

ESPON 2,1,1, SASI Model ESPON 2,1,1, SASI Model ESPON 3.2 Mcrit

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5.1. Application to EU transport policy The model is applied to priority, ‘‘quick start’’ transport projects proposed inside the TEN-TINA future network by Commissioner Karel Van Miert to the EU Council and Parliament (Map 1). Time scale is 2020, when all priority projects are supposed to be realised; only quantitative impact evaluations are introduced. The dimensions or criteria on which impacts are assesses are a simplified sample of the ones previously indicated, especially due to lack of data for all countries of the EU at such detailed territorial level; they are:  territorial efficiency: internal connectivity (E1), external accessibility (E2), GDP growth (E3);  territorial quality: congestion (Q1), emissions (Q2), transport sustainability(rail/road shift) (Q3);  territorial identity: creativity (I1), cultural heritage (I2), landscape fragmentation (I3). For each of these criteria, the appropriate (9) indicators are defined (Table 1); for the time being, an equal weight system is used (1/3 for the three criteria and for their three internal sub/ criteria). In Table 2 the variables used for the territorial sensitivity functions are listed (desirability/utility and vulnerability/receptivity). For each of them, the data sources and the variability intervals, subjectively chosen, are presented. As it was said before in the theoretical presentation of the model, impact variables are normalised inside an interval

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subjectively defined by the model builder. Also the variables expressing desirability and vulnerability are normalised in a similar way: as they are treated as correcting coefficients of the impact values, they are normalised inside the interval 0.8/1.2 (therefore correcting the impacts by 20%, according to the regional conditions). In Table 2 also the form of these functions is presented, being either linear or exponential. The results of the simulations are presented in a spreadsheet (Fig. 3a and b), showing:  the macro-criteria: territorial efficiency, quality, identity and the respective weights,  the nine sub-criteria and their respective weights,  the impact (‘‘TIM weighted mean’’) for each macro-criterion and the aggregate impact,  the values emerging from the previous run, which is presented for the sake of comparison and evaluation of the sensitivity of the model to changes in some parameters (weights, variability intervals, functional form),  the values of impacts on the single regions (NUTS-3 regions) in the ESPON space,  an histogram for each of these regional impacts, indicating impacts on the three macro-criteria, the general impact (full colour bars) and the respective results of the previous simulation (profile bars). The single regional impacts, as well as the regional values of the utilised variables, are presented in single ancillary sheets (PIM,

Table 2 Sensitivity functions and variables. 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=1 S = D norm

LOG [km road + rail]/GDP

0.8–1.2

Linear

ESPON 3.2

D = LOG [current accessibility]

LOG [# of people daily accessible by car]

0.8–1.2

Nonlinear

ESPON 2,1,1–SASI Model

S_E2

Mcrit ESPON 3.1

R=1 S = D norm S_E3

D = GDP 2000 PPP per inhabitant R=1 S = D norm

GDP 2000 PPP per inhabitant

0.9–1.2

Linear

ESPON 3.1, Eurostat Regio

S_Q1

D = Present congestion

D = Million vehicles/network km

0.8–1.2

D = Nonlinear

ESPON 3.2–Mcrit; BBR Corine landcover

V = Share of natural areas S = mean of normalised D and V

V = share of natural areas (km2)

D = Present emissions

Present emissions CO2 year 2000 [million tons] V = share of natural areas (km2)

0.8–1.2

D = Nonlinear

ESPON 3.2–Mcrit; BBR Corine landcover

0.9–1.2

V = Linear

D = Present share of railways on total tran. ntw. R=1 S = D norm

km/km (%)

0.8–1.2

D = Nonlinear

S_I1

D = GDP 2000 PPP per inhabitant R=1 S = D norm

GDP 2000 PPP per inhabitant

0.9–1.2

Linear

ESPON 3.1, Eurostat Regio

S_I2

D = GDP 2000 PPP per inhabitant R=1 S = D norm

GDP 2000 PPP per inhabitant

0.9–1.2

Linear

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

0.9–1.2

Linear

ESPON 1,3,1; GTK

S_Q2

V = Share of natural areas S = mean of normalised D and V S_Q3

ESPON 3.2 Mcrit

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Fig. 3. The impact values sheet: (a) weights and aggregate values; (b) disaggregate values (NUTS-3).

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Map 2. Impact on territorial efficiency.

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Map 4. Impact on territorial identity.

Map 3. Impact on territorial quality. Map 5. Global territorial impact.

TIM, Sensitivity; not shown here). In the desirability and vulnerability sheets the ‘‘levers’’ of the model are presented, that is the elements on which the operator can intervene: variability intervals, functional form, weights.11 The impacts on territorial efficiency, quality and identity and the general impact are subsequently mapped at NUTS-3 level (see Maps 2–5). The following results may be easily seen: (a) the most important advantages (positive impacts) in terms of territorial efficiency are localised inside a wide centralEuropean belt running north-south, encompassing also the western regions of New Member States; in the north-eastern Italian regions, mid-Adriatic and southern-Tyrrhenian regions; in all regions of the Iberian peninsula lying on the Lisbon–

11 





In fact, the flexibility and interactivity of the model manifest themselves: in the possibility of changing interactively the weights of criteria and sub-criteria (u), in the possibility of changing the variability intervals of impact variables (PIMr,c), desirability (Dr,c) and vulnerability (Vr,c) variables, in the possibility of changing the form of desirability and vulnerability curves.

Madrid–Barcelona axis. The lower impacts are recorded in north-western French regions, in the United Kingdom, Ireland and in northern Scandinavian regions; (b) the (negative) impacts on territorial quality are the highest on some already congested axes (Paris–Lille, the Rhone Valley, the Mediterranean coast from Barcelona to Murcia, the axes London–Liverpool and Bologna–Verona) and in some sensitive contexts such as the alpine regions (Trento, Bolzano, Tirol), some Pyrenean regions and some relatively untouched regions of central Italy (some provinces in Tuscany and Umbria). It is important to note also some positive impacts, thanks to the possible modal shifts towards more sustainable transport means: on the already mentioned Lisbon–Barcelona axis, in many German regions, in some scattered regions in New Member countries; (c) impacts on territorial identity appear by and large positive, as TENs will enhance visibility, accessibility and therefore the market potential of many regional ‘‘vocations’’ linked to the natural and cultural heritage: this effect is particularly evident in some east European countries (Czech Republic, Slovakia, Hungary) and in western Ireland. Some negative impacts are

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nevertheless evident, and are linked to possible effects on landscape fragmentation; (d) the general impact is by and large positive (with the equal weight pattern) and particularly so in some large territorial belts: in western regions of New Member States (and in some of these countries overall) and along the east-west central axis in the Iberian peninsula.12 6. Conclusions The TEQUILA model represents an effort towards the construction of a methodology of Territorial Impact Assessment at the same time consistent in conceptual terms – given the persistent absence of a shared procedure – and workable. The concept of territorial cohesion gives the basic guidelines for achieving the first aim; the multicriteria analysis supplies an operational tool for achieving the second. Territorial efficiency, quality and identity represent the dimensions and the macro-criteria on which an impact assessment can be built. Availability of some impact studies of European transport policies and their priority projects on single territorial dimensions (GDP, emissions, etc.) for the European regions allowed us to realize a full application of the model, in its operational form (SIP: Interactive Simulation Package); impacts were elaborated at the NUTS-3 level on the ESPON space (EU plus Norway and Switzerland). Results look convincing and consistent, and encourage the strengthening of the methodology through appropriate scientific and operational advances. References Beinot, E., & Nijkamp, P. (Eds.). (2007). Multi-criteria analysis for land-use management. Berlin: Springer-Verlag. Camagni, R. (1998). Sustainable urban development: Definition and reasons for a research programme. International Journal of Environment and Pollution, 1, 6–26. Camagni, R. (2002). On the concept of territorial competitiveness: Sound or misleading? Urban Studies, 13, 2395–2412. Camagni, R. (2006). Territorial impact assessment – TIA: A methodological proposal. Scienze Regionali – Italian Journal of Regional Science, 2/2006, 135–146.

12 It is worth mentioning that the TEQUILA model was built inside the ESPON 3.2 project, concerning the creation of territorial scenarios in the EU, but was also utilised in ESPON 2.4.1 project, addressed towards the assessment of the territorial impact of environmental policies (civil protection, biodiversity and hydro-geologic policies). In this last case, some interesting and sufficiently counter-intuitive results were achieved, though inside experiments conducted at the aggregate territorial scale (Europe and nation states, NUTS 0 and 1) or on only some case study regions. Cause–effect relationships of each policy on the dimensions of territorial efficiency, quality and identity were highlighted and impacts were subsequently calculated through a qualitative scoring based on expert judgement. While policies of civil protection were generally beneficial on all the three dimensions, policies concerning biodiversity and water resources have shown sometimes – but not always! – some negative impact on territorial efficiency, calling for the necessity of support measures in favour of some sensitive regions. Nowadays Tequila is being used for the assessment of CAP policies.

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