an integrated evaluation model

AN INTEGRATED EVALUATION MODEL FOR SHAPING FUTURE RESILIENT SCENARIOS IN MULTI-POLE TERRITORIAL SYSTEMS VANESSA ASSUMMAa, MARTA BOTTEROa ROBERTO MONACOa, ANA JACINTA SOARESb

a

Department of Regional Studies and Planning (DIST), Politecnico di Torino e-mail: [email protected]; [email protected]; [email protected]

b

Centre of Mathematics, Universidade do Minho Braga e-mail: [email protected]

How to cite item in APA format: Assumma, V., Bottero, M., Monaco, R. & Soares, A.J. (2018). An integrated evaluation model for shaping future resilient scenarios in multi-pole territorial systems. In A. Leone & C. Gargiulo (Eds.), Environmental and territorial modelling for planning and design. (pp. 17 52 -- 24 55). Naples: FedOAPress. ISBN: 978-88-6887-048-5, doi: 10.6093/978-88-6887-048-5

ABSTRACT In the present paper an integrated evaluation procedure is considered, with the aim of evaluating the ecological and economic synergies within an environmental system. The case study chosen to test this procedure is the Monferrato Ovadese in the province of Alessandria (Piedmont, Italy). The territory, which includes almost 40 municipalities, was divided into a system of 11 clusters, that were treated as interactive poles of the territory itself. This integrated procedure uses a system of ecological and economic indicators. For landscape ecology, the indicators were introduced as coefficients, which predict the transformation scenarios of the environmental system under observation. For the economics, indicators were integrated through a Multicriteria Analysis in order to define a super-indicator, which may be interpreted as a measure of attractiveness of the poles inside the environmental system. The attractiveness was subsequently set as the critical parameter of a dynamical system represented by the mathematical model of a Lotka Volterra type, which simulated people flows within the territory. This study demonstrates how this novel evaluation procedure can be used to support the decision-making process in the choice of sustainable territorial and urban actions.

KEYWORDS Indicators and indexes; Mathematical modelling; Multicriteria Analysis; Integrated Evaluation Model

V. Assumma, M. Bottero, R. Monaco, A.J. Soares

1

INTRODUCTION

The preservation and enhancement of the whole environmental system represent one of the main challenges of the Anthropocene (Crutzen, 2005). Assessing the resilience of environmental systems is currently considered to be a field with high margins of development and improvement at all scales (Cutter, 2016). There is an urgent need to shape territorial transformation scenarios and support the governance in preserving environmental systems. The development of evaluation methodologies could provide useful support for the planning, and in this context, the integration of them in multidisciplinary and interdisciplinary terms may carry useful insights (Cutter, 2016; Pearce & Turner, 1990; Sharifi, 2016). The present paper will assess the resilience through an integrated evaluation model, which considers a system of indicators and indexes to measure the resilience capability in ecological and economic terms and employs a mathematical model to investigate people dynamics over time. This evaluation model has been employed in a real case study in Piedmont region (Italy): the Monferrato Ovadese. The paper is structured as follows: section 2 focuses on the methodology; section 3 proposes a description of the case study; section 4 describes the results obtained by the system of ecological and economic indicators and by the application of a mathematical model of a Lotka-Volterra type; section 5 concludes with some remarks and future perspectives.

2

METHODOLOGY

This paper proposes an integrated evaluation methodology, which considers a system of indicators and indexes to assess the resilience of the municipalities of the Monferrato Ovadese in ecological and economic terms as well as a mathematical model, employed to investigate the people dynamics over time and shape resilient scenarios. More specifically, we consider a system of indicators which can be divided into two subsystems, namely the ecological indicators and the economic indicators.

2.1 THE SYSTEM OF INDICATORS Ecological indicators Generally, complex systems are interesting because they show a non-linear dynamics which affect the ecological stability of territories at macro and micro scales (Folke, 2010; Gunderson & Holling, 2002; Holling, 1973). Additionally, this dynamics can be also influenced by the impact of human activities. For these reasons, the sub-system of ecological indicators considers specific variables deduced by GIS data and evaluated for all municipalities of the considered area (Tab. 1). Economic indicators The sub-system of economic indicators is useful to assess the economic value of the Monferrato Ovadese. The economic indicators are organized according to the “value tree” approach proposed in the Multicriteria Decision Analysis (MCDA), which is generally employed to solve complex problems (Saaty, 1980). This is an appropriate technique for the assessment of environmental systems (Assumma et al., 2016; Assumma et al., 2017, 2019; Brunetta et al., 2017, 2018; Tagliafierro et al., 2013). The economic indicators and indexes are described in Tab. 2. It has to be noticed that the economic indicators have been assessed for all Municipalities in the area under investigation.

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An integrated evaluation model for shaping future resilient scenarios in multi-pole territorial systems

Ecological Indicators

Unit

vi

%

mi

hi

Ui

Ci

Extent of green areas of high ecological quality

Formulas Vi=∑Av with BTC > 2,4/ Atot

where Av=green areas with BTC > 2,4 Atot= total area of the system 2 Mcal/(m ∙year) mi=∑mij=1Bji∙sji

Biological energy

Dispersion of urban areas

where Bij= BTC index of biotope j ∈ sector i sji=surface of biotope j ∈ sector i hi=∑Pe/Ptot

%

Intensity of urban areas

where Ptot= Total perimeter of the system Pe= Perimeter of urbanized areas Ui=1 - Ae/Atot ≤1

%

Connectivity index

where Ae= area of urbanized areas Atot= total area of the system ci=∑k∈Ii (Bi+Bk)/(Bimax+Bkmax)∙ Hik≤1

%

c=1/n∑ni=1ci

ri

ki

Intensity of impermeable barriers

Dispersion of impermeable barriers

%

where Bi and Bk= BTC of I and k ecological sectors Bimax and Bkmax=maximum BTC of i and k ecological sectors Hik= total length of the barrier between sectors i and k with a permeability index ≤1 ri=∑Aip/Atot

%

where Aip= area of impermeable barriers Atot= total area of the system ki=∑Pip/Ptot Tab. 1 The system of ecological indicators defined from GIS data

2.2 EVALUATION OF THE INTEGRATED INDEX The ecological and economic indicators have been aggregated in two specific indexes that represent the ecological and economic values of the system under investigation. The ecological index is calculated as the arithmetic mean of the values of the related indicators, whereas the economic index comes from a weighted average of the related indicators, according to the weights defined with a multidisciplinary panel of experts based on the AHP (Saaty, 1980). The last step of evaluation consists in the calculation of an integrated ecological-economic index, say Ai, that is the average between the ecological and economic indexes previously calculated. Such index aggregates the ecological and economic values of the system, and thus this step provides an overall vision of the considered territory.

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Component

Economic Indicators

Unit

Economic Indexes

Agriculture

DOP Farms

no.

Adop= DOP Farms [C]/Tot. Farms [S]

BIO Farms

no.

Abio= BIO Farms [C]/Tot. Farms [S]

Workers in DOP Farms

no.

Wdop=Workers in DOP Farms [C]/ Tot. Workers in Farms [S]

Workers in BIO Farms

no.

Wbio= Workers in BIO Farms [C]/Tot. Workers in Farms [S]

Utilized Agricultural Surface

ha

UAS= UAS [C]/ Area [S]

Arrives

no.

TUR= arrives [C]/presences [C]

Presences

no.

Total beds

no.

PT= Total beds [C]/ Total beds [S]

Beds in agritourism

no.

PL= Beds in farmhouses [C]/ Total beds [S]

Tourism

Real Estate

Forestry

Real estate value of building residences €/m2 VIR= Average real estate value [C]/ Average real estate value [N] Average Agricultural Value

€/ha

VAM= Average agriculture value [C]/ Average agriculture value [N]

Forestry farms

no.

Afor= Forestry farms [C]/ Area [S]

Forestry surface

ha

Sfor= Forestry surface [C]/ Area [S]

Forestry workers

no.

Wfor=Workers in forestry farms [C]/ Workers in forestry farms [S]

Notes: C= cluster, S= System, N=Nation Tab. 2 The system of economic indicators employed to assess the resilience capability of the case study (Source: Assumma et al., 2016, 2019; Bottero, 2011; Brunetta et al., 2018)

3

CASE STUDY: DESCRIPTION OF THE TERRITORY

The Monferrato Ovadese is a multi-pole territorial system of Piedmont, which extends for 60.000 hectares between the province of Alessandria and the region of Liguria. The lands of the Monferrato Ovadese are defined as “middle lands” because the historical settlements developed according to the geomorphological characteristics and the territorial vocation to cross the Appennines for commercial purposes. Moreover, the Monferrato Ovadese touches the Unesco site “Vineyard landscapes of Piedmont, Langhe, Roero and Monferrato" (2014) by Strevi, and this represents an opportunity of growth and development, especially due to the influence of both Piedmontese and Ligurian history, culture and traditions (Assumma et al., 2017). In detail, the territory under examination is constituted by 37 municipalities that have been structured in 11 homogeneous territorial clusters described as follows, CL1 (Novi Ligure)

CL2 (Arquata Scrivia)

CL3 (Pasturana)

CL4 (Basaluzzo)

CL5 (Silvano dOrba)

CL6 (Lerma)

CL7 (Ovada)

CL8 (Predosa)

CL9 (Rocca Grimalda)

CL10 (Cremolino)

CL11 (Strevi)

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4

RESULTS

4.1 CALCULATION OF THE OVERALL ATTRACTIVENESS Following the methodology described in section 2, a spatial visualization of results is illustrated in Figg. 3a, 3b and 3c. The Clusters 1 and 2 record high economic values because there are many industrial activities. The Cluster 11 also shows very high economic and ecological values, due probably to the proximity to the Unesco site. By contrast, the Clusters 3, 4, 7 and 10 show average values. Specifically, the Clusters of Novi Ligure (CL1) and Arquata Scrivia (CL2) record very high economic values, due to the presence of one of the biggest fashion outlets in Europe and many other commercial poles, which promotes a continuous flow of people. Therefore, such Clusters record medium to poor ecological quality, because of the commercial vocation of these territories. The Cluster of Lerma (CL6) records a poor economic value, because its activities are focused on a seasonal tourism and the economy is based on the local cultivation of different products, including production of wine. Moreover, the Cluster 6 records a very high ecological quality because it includes protected areas of the Piedmontese Appennine.The Cluster of Strevi (CL11) records both a very high ecological quality and a high economic value, mainly due to the fact that Strevi represents the “natural” door of the Unesco site. This also determines positive effects in ecological and economic terms for Clusters 8, 9 and 10. The economic and ecological indexes have been aggregated in a final spatial index, the Total Attractiveness as illustrated in Fig. 3c. The index of Total Attractiveness represents a positive frame of the Monferrato Ovadese, with good and very high values. The only exception is recorded by the Cluster 5, which maintains anyway a medium value of Total Attractiveness.

a

b

c Fig. 3 Spatial results of economic values (a), ecological values (b) and total attractiveness (c) (Own elaboration, 2018)

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4.2 THE MATHEMATICAL MODEL In this paper, the integrated indicators have been introduced as coefficients of a system of ordinary differential equations of a Lotka-Volterra type proposed by Monaco & Rabino (1984) and then revisited in recent contributions (Assumma et al., 2016; Monaco & Servente, 2006; Monaco, 2015;). This mathematical model is used in the simulation of population dynamics, in connection with the attractiveness factor. In addition, the model predictions are used in the interpretation of the population dynamics in terms of resilience factor. The first term of equation (3) takes into account the attractiveness Ai for people present in the pole i through a logistic expression (Murray, 2002), while the second term takes into account the attractiveness of pole i on people present in others poles j. The mathematical model used here takes the form: P’i=AiPi(t)(1-Pi(t)/Si)+∑nj≠i Ai/Aj[1- (dij/dM)Pj(t)

i=1, 2,..., n

(3)

where P’i= time derivative of Pi Pi= population present in the pole i Ai and Aj= attractiveness index of poles i and j dij= distance between the poles i and j dM= maximum distance recorded between poles Si= threshold of maximum number of people in the pole i We use the model presented in this section to study the population dynamics on Piedmont, a region in northern Italy that shares different structuring and qualifying landscape components. Fig. 4 shows the time evolution of all populations in the time interval [0; 0,05] (the time is measured in arbitrary scale).

Fig. 4 Time evolution of all populations in the interval (0; 0.05). Own elaboration, 2018

Thanks to the ecological and economic attractiveness, the mathematical model can predict the potential variation of populations present in the considered multi-pole territorial system. The results obtained are illustrated in Fig. 4.

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5

CONCLUSIONS

The integrated evaluation methodology presented in this paper provides useful insights to assess the resilience of the Monferrato Ovadese in ecological and economic terms. This innovative tool can support the decision-making process to shape future resilient scenarios also to answer the domain of prevention and mitigation of natural disasters and human activities. As a future perspective, a sensitivity analysis can be useful increase the robustness of the set of weights considered. With regard to the mathematical model, further studies would be useful, in particular if collecting a wider set of data to make simulations in the past with the purpose to improve the adequacy of the mathematical model. Another future perspective could be the use of this integrated evaluation methodology in a real land use intervention with the purpose of planning scenarios of decision-making utility.

ACKNOWLEDGEMENTS The present research has been partially supported by the GNFM of INdAM and by the Portuguese Funds FCT Project UID/MAT/00013/2013, within the activities of the Risk Responsible Resilience Interdepartmental Centre (R3C) DIST - PoliTO. One of the Authors (AJS) thanks GNFM for the financial support given in her visiting professor program in Italy.

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AUTHOR’S PROFILE Vanessa Assumma is a Ph.D. student in Urban and Regional Development at Interuniversity Department of Urban and Regional Studies and Planning, Politecnico di Torino, Italy. Scientific interests on Regional and Urban Economics, Planning Evaluation, Scenario building and Mathematical Models in Environmental Sciences.

Roberto Monaco is a full Professor at the Faculty of Architecture of Politecnico Torino, Italy. Scientific interests on Kinetic Theory of Gases, Numerical Simulation Aspects of Molecular Fluid-dynamics Wave Propagation Problems in Hyperbolic Systems, Ordinary Differential Equations with Random Terms, Mathematical Models and Dynamical Systems in Environmental Sciences. Marta Bottero is an associate Professor at Interuniversity Department of Regional Studies and Planning at Politecnico di Torino, Italy. Scientific interests on Sustainability Assessment, Planning Evaluation, Project Appraisal, Regional and Urban Economics. Ana Jacinta Soares is an associate Professor at the Mathematics Department and member of the Centre of Mathematics of the University of Minho, Braga, Portugal. Ph.D. in Mathematics. Scientific interests on Mathematical Physics, Differential Equations, Modelling and Applied Mathematics.

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