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2020 | OriginalPaper | Chapter

3. The Pioneering Quantitative Model for TIA: TEQUILA

Author : Roberto Camagni

Published in: Territorial Impact Assessment

Publisher: Springer International Publishing

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Abstract

The request for building an operational model for the ex ante assessment of the territorial impact of EU policies, projects and regulations was addressed directly to the author by the ESPON managing authority. A rationale and definition of what could be intended as TIA was proposed, a prototype model and the connected software was built and applied to the TEN (Trans-European Network) program in 2004–2006. The convincing results achieved were followed by subsequent new and deeper studies, where the model was improved, simplified and implemented on EU transport and agricultural policies and to some EU directives in the environmental fields. TEQUILA is a multi-criteria model working on a quantitative base on Nuts3 regions in the EU; however, it integrates in a statistically consistent way qualitative judgements by experts, when necessary. The criteria refer to the main dimensions of territorial cohesion – territorial efficiency, territorial quality and territorial identity – and their sub-dimensions/criteria, measurable by multiple indicators. Particularly the goal of territorial identity captured the interest and favour of policy makers. Impact maps on concrete applications, illustrated here, were used in official reports of the European Commission.

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previous chapter TARGET_TIA: A Complete, Flexible and Sound Territorial Impact Assessment Tool

next chapter STeMA: A Sustainable Territorial Economic/Environmental Management Approach

In that period, 1997–1998, I was serving as head of the Department of Urban Affairs at the Presidency of the Council of Ministers in Rome, under the first Prodi government.

In the final paragraph of the ESDP (326), where the case of cultural sites and art cities is dealt with, particularly with reference to tourism and “property market speculation” threats, similar spatial development strategies integrating different approaches are suggested, but not a formalised impact assessment tool (probably due to the immaterial costs and benefits which are intrinsic in Cultural Heritage (CH). However, as it will be shown later, some reliable quantitative measurements are possible also in the field of CH. A recent econometric work on the relationship between CH and development through such immaterial processes as inspiration and creativity has produced solid and convincing results. See: Cerisola 2019.

A careful scoping document on the subject was produced by Williams et al. 2000, confirming the non-existence of such a tool.

The subsequent listing of the empirical issues encompassed by the new concept look more interesting: the concentration of economic activity and population in the European “Pentagon”; the imbalance between metropolitan areas and the rest of the countries; growing congestion, pollution and social exclusion in the main conurbations; the presence of rural areas suffering from inadequate accessibility; urban sprawl.

Two relevant innovations are present here. Firstly, traditional “spatial development” policies are called “territorial”; secondly, the concept of ‘territorial capital’ is used for the first time, implicitly suggesting that territory is a resource, generating productivity increases (“higher return for specific kinds of investment”) and wellbeing to local communities. On the meaning and use of the territorial capital concept, see Camagni 2019.

As in the usual performance matrix of MCA showing scores (in our case the impacts of a policy), on columns we find the dimensions/criteria, but on the rows, we do not find alternatives (different projects or alternatives of the same project) but regions. In our case, the goal is not to choose among alternatives as in multi-criteria decision analysis, but to compare impacts on different regions on the basis of consistent quantitative scoring on each single dimension/criterion, the goal being to detect spatial cases where strong mitigation measures should be provided or alternative implementations of the policy investigated. Inside the matrix, weighted summation can be made by column (summative evaluation of impacts on single regions) or by rows (giving a general assessment of the policy impact on the entire EU territory in each dimension/criterion).

For the consideration of the specific priorities of local communities, see later at point l.

On the positive side: 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. Thanks to the linearity hypothesis, scores in the 0–5 interval are easily transformed in the normal intervals, if needed.

In algebraic terms, when n stands for normalised scores and o for observed or calculated values, the normalised value nx of any observed ox is given by:

$$ nx=\frac{\left({n}_{\mathrm{max}}-{n}_{\mathrm{min}}\right)}{\left({o}_{\mathrm{max}}-{o}_{\mathrm{min}}\right)}\cdot \left({o}_{\mathrm{max}}- ox\right) $$

Weights are at the same time ‘importance’ coefficient – indicating the priorities of national or regional communities, for example with respect to the common development-environment trade-off – and ‘substitution indicators’, i.e. marginal substitution rates among impacts on different criteria, allowing compensations among them. The author prefers not to add a third, unconventional role to weights, especially if different solutions can be found.

In the example of Fig. 3.1, the values of calculated impacts of an abstract development measure on the different regions (x axis) are very similar: why then should we attribute 0, meaning a hell condition, to 180 new jobs and 5, meaning heaven, to 250? Better to distribute scores between 2 and 3, half point around the average.

But it can be implemented easily: see footnote 6.

For example: in the case of a technological risk, PIM gives the probability of explosion of a given plant, and V_r the damage in case the plant is located close to a city or in an inhabited site.

In this case, if max % change allowed is 20%, for a region with a per capita GDP equal to the EU average the coefficient will be 1 (no change); for the poorest region it will be 1,20 and for the richest region 0,80. Therefore the same increase in GDP will have a superior value in the poor region and an inferior (perceived) value in the rich one.

The political relevance of ‘outliers’ – i.e. of excessive impacts on some aspects, population classes or regions – is made explicit by the European Commission: “When a single Member State or region is disproportionately affected (so-called ‘outlier’ impact), this should be mentioned. Where such disparities appear to be significant, they should be analysed as they may be a reason to adapt the initiative, for instance to offer mitigating or transitional measures for the ‘outlier’” (EC 2009: 41).

Mapping procedures directly integrated inside the computational machine were finalised only in the TIP-TAP version of the TEQUILA model.

The choice of this policy field was due to the existence of multiple studies, allowing a quantitative territorial assessment. Collaboration with the teams involved in these studies was gratefully acknowledged.

Ten years ago the identitarian issue was not so politically clear as today; policy makers, academics and practitioners proved to be closer to people’s feelings than public officers and experts.

Exposure fields are in a fixed number, but each directive activates only some of them.

This condition was not met in case of our assessment of CAP policy, due to the national responsibility on inter-regional resource allocation.

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Title: The Pioneering Quantitative Model for TIA: TEQUILA
Author: Roberto Camagni
Publisher: Springer International Publishing
Book: Territorial Impact Assessment
Print ISBN: 978-3-030-54501-7

Electronic ISBN: 978-3-030-54502-4

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-54502-4_3

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