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Article Landscape Assessment and Economic Accounting in Wind Farm Programming: Two Cases in

Salvatore Giuffrida, Filippo Gagliano, Francesco Nocera * and Maria Rosa Trovato Department of Civil Engineering and Architecture, University of (), 95125 Catania, Italy; [email protected] (S.G.); [email protected] (F.G.); [email protected] (M.R.T.) * Correspondence: [email protected]; Tel.: +39-095-738-2366

 Received: 18 August 2018; Accepted: 11 October 2018; Published: 15 October 2018 

Abstract: In recent years, the scientific interest for the economic and landscape impact of wind farms has increased. This paper presents a useful GIS tool that allows for helping policymakers and investors to identify promising areas for wind power generation as well as landscape impact and financial and economic sustainability of wind farms. The results of the research carried out for exploring the potential for wind energy in two territorial contexts of Sicily region are presented with the particular look at the possibilities of economic developing, stakeholders’ opportunities and obstacles in the policy, legal, and regulatory framework.

Keywords: wind energy; landscape impact; GIS tools; economic-financial valuation; DRSA decision making pattern

1. Introduction The Climate and Energy package passed by the European Union (EU) to meet the goals of the Kyoto Protocol with the intention of preventing climate instability, known as the 20 20 20 Plan (Directive 2009/29/EC) [1], provides for joint action on CO2 emission levels (−20%), energy production from renewable sources (+20%) and primary energy saving (−20%) by the end of 2020, with a commitment to reduce emissions by 50% to the end of 2050. The outline of these courses of action also specifically considers local and business policies—carbon footprint measurements of products, RET (Renewable Energy Target) incentives, CO2 compensation through urban forestation, as well as environmental, energy, and climate education and training. These embrace aspects that are deeper than those generically indicated by the environmental and economic policy tools that rely on “command and control logic” and that refer to the compensatory/commercial approach of the Coasian matrix. The regional energy policy in Sicily is governed by the Environmental Energy Plan of the Sicily Region (Piano Energetico Ambientale della Regione Siciliana—PEARS)[2], which identifies wind energy as the best opportunity for Sicily to produce clean energy and reduce pollutant emissions. As a result, planning its use must be optimised, given the high production potential offered by the conformation of the Sicilian territory and its anemometric characteristics. Since these are joint economic, territorial, and landscape issues, there are two main issues that planners and Decision Makers in this sector must address: on one hand, the amount of installed power that Sicily can/must provide, and, on the other, the location/size of the plants. These topics involve wide ecological and environmental issues that are profoundly affected by the scientific assessment methods and content, precisely because it is impossible to provide evidence for or against this kind of energy production, especially when comparing the overall supply chain with that of fossil fuels. Consequently, opinion on wind energy varies significantly. This opinion is positive if we take a deep long-term ecological view and worry about the progressive shift of environmental and human cost towards the most disadvantaged areas of the planet. It views territorial containment,

Land 2018, 7, 120; doi:10.3390/land7040120 www.mdpi.com/journal/land Land 2018, 7, 120 2 of 20 by companies and homes, and the outcomes of their needs and relative economic performance on the environment and landscape, so that they are “aware” of the effects their lifestyles have [3,4]. More restrictive and fundamentally pessimistic views about the possibility of substantially withdrawing fossil fuels, sees the development of wind farms as yet another excuse for needless infrastructure, plundering the territory and violating the “landscape structure” [5,6]. This leads to an imbalance between local decisions/negotiation and “broadcasting” with large scale impact, which is done blindly when compared to strategic planning. The financial compensation is insignificant in comparison to the value of the landscape and those who regret its loss after the fact. Financial incentives and compensation introduce new, sometimes distortional [3], extraneous, and abstract rules about how we relate to the landscape, with the total, personal and collective experience that identifies individuals and communities. These rules are intended to be long-term and linked to the thin thread of delicate balance. Finally, the susceptibility of the political/administrative system to specific interests and corruption completes this critical picture [7,8]. The proposed contribution aims to address assessment and planning at a provincial scale, with reference to two different territorial contexts, integrating the set of tools needed to define the plant location models into a descriptive, semantic and syntactic model. The specific objects are:

- the descriptive model provides the factual elements useful for wind farm positioning; - the semantic model associates each wind farm with attributes of economic value and landscape; and, - the syntactic model defines the decision rules for the formation of the best territorial allocation layout of wind farms in the two provinces examined.

2. Materials The territories of the two provinces, and Siracusa present wide differences as regards their location in the geographical context, the orography, the degree of anthropization, and the quality of the natural and cultural landscape. The occupies the central part of the island for an area of 2574.7 km2 and it includes 20 municipalities, with a total population of 168,737 inhabitants [9]. The province of Siracusa occupies the southeastern part of the island for an area of 2109 km2 and includes 21 municipalities, with a total population of 400,881 inhabitants [9]. The result is a considerable difference in population density, 65.5 inhab./km2 Enna versus 190.1 inhab./km2 Siracusa and consequently a different degree of urbanization and infrastructure of the territory. For these purposes, the structured and rich coastal area in the territory of Siracusa, which attracts investments and tourism, with remarkable impacts on the real estate market prices expectations [10,11], differs significantly from the territory of Enna, which is rather introverted and much less rich also from the point of view of wealth capitalized in real estate [12]; its economy, originally focused mainly on the primary (agricultural, mining etc.) sector, is now growing also in the cultural heritage ones and numerous projects aimed at the territorial restructuring are now in progress [13]. From the orographic point of view the two provinces present significant differences (Figure1):

- the province of Siracusa is characterized by an extended and jagged waterfront, 447.6 km equal to 89% of the total perimeter of its territory (503.4 km), and a wide flat belt formed in the north by a portion of the alluvial and the Etna lava lands that give life to vast cultivations citrus; to the east a narrower flat belt, densely urbanized, and industrialized; the southern part the coastal landscape is shaped by the action of the sea waters, with the formation of wide marshes, and of the rivers that flow on the wide karst plateau; the central part of the province extends over the Ibleo plateau, a mountain range that was formed by a massive calcareous-marly white conch of the Miocene (Plateau of the Hyblaean); the Ibleo plank has a mainly hilly layout increasing in altitude towards the hinterland to the west, where it is dominated by high reliefs: Lauro Mount Land 2018, 7, 120 3 of 20

(986 m a.s.l), Contessa Mount (914 m a.s.l), Casale Mount (910 m a.s.l), Santa Venera Mount (869 m a.s.l) and Erbesso Mount (821 m a.s.l); some relevant Iblean reliefs are Pancali Mount (485 m a.s.l.) and the Climiti Mounts (416 m a.s.l.) that face the Gulf of Augusta; the Ibleo plateau slopes towards the sea maintaining an average altitude of about 300 m a.s.l.; and, - the province of Enna covers the central part of the island, is the only one in Sicily that has no access to the sea, and is 18 km from the Tyrrhenian Sea to the north, 28 km from the Channel of Sicily to the south, 26 km from the Ionian Sea to East; within its mountainous territory important geographical signs converge, the ridges of the three main mountain ranges of the island, the Nebrodi, the Madonie and the Erei, which were the geographical limits of the three administrative districts during the Islamic and later Norman periods (828–1072), Val Demone, Val di Noto and Val di Mazzara; consequently, on the one hand, the ridges of these three mountain chains are the most suitable locations for the wind farm, on the other, these locations generate the greatest landscape impact both for the extensive intervisibility of the plants and for the interference with the migratory routes of the birdlife; a further aspect that affects the suitability of the location of wind farms is the roughness of the territory that is characterized by the reliefs that dampen the wind speed at low altitude, making it necessary to install higher wind turbines; remarkable mountain reliefs in the province of Enna are: Sambughetti Mount (1558 m a.s.l.) Altesina Mount (1192 m a.s.l.), Rossomanno Mount (885 m a.s.l.), Torre Mount (643 m a.s.l.); the lowest point in the province of Enna is 61 m a.s.l.

Figure 1. Geographical frame and orography of Enna and Siracusa provinces.

3. Methods The main purpose of this study is the formation of a decision model to support energy policy that is focused on renewable wind power [14,15]. In the context of the Sicilian territory, a comparative study is proposed on two provinces of Siracusa (SR) and Enna (EN), highlighting how some significant differences affect the structure of investment planning and the consequent impact on the landscape. The primary differences between the two provinces concern the natural substratum, i.e., the territory and the windiness, and the cultural contribution, i.e., the complex of the landscape values testifying the equilibrium and the tensions stratified in the millennial adaptation process of the local communities to the natural constraints and opportunities that have influenced the settlements. The applied approach is divided into three models, a descriptive one, which aimed at selecting the areas suitable for the location of the plants, a semantic one, aimed at evaluating the economic and landscape profile, a syntactic one, aimed at providing the rules for the optimal layout choice of the wind farm policies at the provincial scale. Land 2018, 7, 120 4 of 20

3.1. Descriptive Model. Selection of the Suitable Plant Areas The descriptive model combines the characteristics of the territory, contained in the Provincial Territorial Landscape Plans (PPTP), with the rules on the implementation of the wind farms, contained in the PEARS [16,17], and it selects the areas suitable for the installments. The model identifies suitable areas excluding the entire territorial surface:

- the 5 km buffer area around each built-up area; - a 500 m bound belt around the archaeological areas; - a 150 m bound zone along or around the water bodies; and, - the Special Protection Zones (SPZ) and the Community Interest Sites (CIS).

Once the suitable areas were identified, the wind turbines were calculated in these areas, based on the type of wind turbines (80 m hub height 2000 kW), assumed equal for all the sites and for the two provinces; this hypothesis simplifies the study by making comparisons easier; on the basis of the results obtained, the model allows for subsequently optimizing the size of the wind turbines to take into account the best relationship between landscape impact and economic profitability. In the province of Enna, where the suitable areas identified are more numerous and more extensive, it has been possible to hypothesize the wind farms layout a 10 × 10 square grid. The PEARS supposes a minimum distance of 4 km between the plant areas, and a minimum distance of 400 m (5 diameters) between the turbines. In the province of Siracusa, given the small number of areas allowed and their smaller size, the turbines are hypothesized to be located along the roads that cross the plant areas.

3.2. Semantic Model. Economic/Landscape Evaluation Since these areas, although formally suitable, give rise to plants with a different economic and landscape profile, it has become necessary to evaluate for each of them the economic profitability and the visual impact related to the different windiness and qualitative characteristics of the topographical contexts of reference. The results of the GIS-based territorial analyses constitute the raw material for the assessment model. In a general sense, this is inspired by the approach of F. Rizzo [18–20], which takes the multifold substance that gives value to the creative combination of matter, energy, and information at the level of the three surpluses: 1. Natural (conservational), 2. Bio-Genealogical (evolutional), and 3. Historical/cultural (argumentative). This approach gives rise to judgement criteria that integrate with the design requirements in the “alternative plan design” stage, and specifically: regarding Surplus 1, minimising physical transformation (locating plants near existent road infrastructures) and CO2 emissions (maximum energy yield per unit of installed power); regarding Surplus 2, maximising the distance from the routes of birdlife; regarding Surplus 3, minimising visual impact with respect to economic convenience, which are respectively qualitative and quantitative measurements of territorial/environmental and social/economic information. Specifically, economic valuation is performed using the main indicators of investment profitability [21], landscape assessment, based on its visual impact and structure [22].

3.2.1. Economic Appraisal Starting from the anemometric measurements, the windiness was calculated from the wind probability density in each of the areas related to the anemometric stations (Figures2 and3), and the number of hours of wind was calculated for the various speeds in each of the sites identified for plants in the two Provinces. The relative speeds measured 10 m above the ground (V2) have been extrapolated w to the hub height (80 m, assuming the maximum turbine efficiency): V80 = V2 × (80/2) , where: V2 is the speed at the measured height (10 m above ground); and, w is a coefficient that generally depends on the roughness of the terrain [23]. Land 2018, 7, x FOR PEER REVIEW 5 of 20 Land 2018, 7, x FOR PEER REVIEW 5 of 20 3.2.1. Economic Appraisal 3.2.1. Economic Appraisal Starting from the anemometric measurements, the windiness was calculated from the wind probabilityStarting density from thein each anemometric of the areas measurements, related to the anemometric the windiness stations was calculated (Figures 2 fromand 3), the and wind the numberprobability of hours density of windin each was of calculatedthe areas related for the tovariou the anemometrics speeds in each stations of the (Figures sites identified 2 and 3), for and plants the innumber the two of hours Provinces. of wind The was relativecalculated speeds for the measured various speeds 10 m in above each of the the groundsites identified (V2) have for plants been extrapolatedin the two Provinces.to the hub heightThe relative (80 m, asspeedssuming measured the maximum 10 m turbineabove efficiency):the ground V 80(V =2) V have2 × (80/2) beenw, w where:extrapolated V2 is tothe the speed hub atheight the measured(80 m, assuming height the(10 maximumm above ground);turbine efficiency): and, w is Va 80coefficient = V2 × (80/2) that, generallywhere: V 2depends is the speed on the at roughness the measured of the height terrain (10 [23]. m above ground); and, w is a coefficient that generallyLand depends2018, 7, 120 on the roughness of the terrain [23]. 5 of 20 Lentini Pachino 14.00 Lentini 14.00 Pachino 12.0014.00 12.0014.00

10.0012.00 10.0012.00

10.008.00 10.008.00

6.008.00 6.008.00

Days/month 4.006.00 Days/month 4.006.00

Days/month Days/month 2.004.00 2.004.00 0.00 2.00 0.002.00 9.8 10.4 10.9 11.4 12.0 12.5 13.0 13.5 14.1 14.6 9.2 9.6 10.1 10.5 11.0 11.5 11.9 12.4 12.9 13.3 0.00 Wind speed (m/s) 0.00 Wind speed (m/s) 9.8 10.4 10.9 11.4 12.0 12.5 13.0 13.5 14.1 14.6 9.2 9.6 10.1 10.5 11.0 11.5 11.9 12.4 12.9 13.3 PalazzoloWind Acreide speed (m/s) AugustaWind speed (m/s) 14.00 Palazzolo Acreide 14.00 Augusta 12.0014.00 12.0014.00

10.0012.00 10.0012.00

10.008.00 10.008.00

6.008.00 6.008.00

Days/month 4.006.00 Days/month 4.006.00

Days/month 2.004.00 Days/month 2.004.00

0.002.00 0.002.00 10.0 10.3 10.7 11.0 11.3 11.6 11.9 12.2 12.6 12.9 12.0 12.3 12.7 13.1 13.5 13.8 14.2 14.6 15.0 15.4 0.00 Wind speed (m/s) 0.00 Wind speed (m/s) 10.0 10.3 10.7 11.0 11.3 11.6 11.9 12.2 12.6 12.9 12.0 12.3 12.7 13.1 13.5 13.8 14.2 14.6 15.0 15.4 Wind speed (m/s) Wind speed (m/s) Figure 2. Calculated windiness 80 m above the ground—Province of Siracusa. FigureFigure 2. Calculated 2. Calculated windiness windiness 80 80 m m above the the ground—Province ground—Province of Siracusa. of Siracusa. Enna Nicosia 14.00 14.00 14.00 Aidone Enna Nicosia 12.0014.00 12.0014.00 12.0014.00

10.0012.00 10.0012.00 10.0012.00

10.008.00 10.008.00 10.008.00

6.008.00 6.008.00 6.008.00 Days/month Days/month Days/month

4.006.00 4.006.00 4.006.00 Days/month Days/month Days/month 2.004.00 2.004.00 2.004.00

0.002.00 0.002.00 0.002.00 1.3 2.0 2.6 3.2 3.9 4.5 5.1 5.6 6.5 7.5 8.4 9.4 10.3 11.2 4.7 5.4 6.0 6.6 7.3 7.9 8.5 0.00 Wind speed (m/s) 0.00 Wind speed (m/s) 0.00 Wind speed (m/s) 1.3 2.0 2.6 3.2 3.9 4.5 5.1 5.6 6.5 7.5 8.4 9.4 10.3 11.2 4.7 5.4 6.0 6.6 7.3 7.9 8.5 Piazza ArmerinaWind speed (m/s) Wind speed (m/s) CalascibettaWind speed (m/s) 14.00 14.00 14.00 Agira 12.0014.00 12.0014.00 12.0014.00

10.0012.00 10.0012.00 10.0012.00

10.008.00 10.008.00 10.008.00

6.008.00 6.008.00 6.008.00 Days/month Days/month Days/month 4.006.00 4.006.00 4.006.00 Days/month Days/month Days/month 2.004.00 2.004.00 2.004.00

0.002.00 0.002.00 0.002.00 2.5 4.5 6.5 8.5 10.4 12.4 14.4 7.8 8.6 9.4 10.2 11.0 11.8 12.6 6.7 8.0 9.4 10.7 12.0 13.3 14.7 0.00 Wind speed (m/s) 0.00 Wind speed (m/s) 0.00 Wind speed (m/s) 2.5 4.5 6.5 8.5 10.4 12.4 14.4 7.8 8.6 9.4 10.2 11.0 11.8 12.6 6.7 8.0 9.4 10.7 12.0 13.3 14.7 Wind speed (m/s) Wind speed (m/s) Wind speed (m/s) FigureFigure 3. Calculated 3. Calculated windiness windiness 80 80 m aboveabove the the ground—Province ground—Province of Enna. of Enna. The productibilityFigure 3. Calculated of the plants windiness located 80 in m the above identified the ground—Province areas, measured in of MW, Enna. is calculated Theusing productibility Betz’s law [24 of,25 the], which plants states located that the in energy the identified extracted from areas, the measured wind is proportional in MW, tois thecalculated usingThe Betz’smass productibility flowlaw that[24,25], passes of which throughthe plants states the rotor locatedthat and the thein ener differencethegy identified extracted in kinetic areas, from energy measuredthe between wind is thein proportional inletMW, section is calculated to the using Betz’sand the law outlet [24,25], section: which states that the energy extracted from the wind is proportional to the 3 2 P = 2·r·A·v1·a·(1 − a) (1) Land 2018, 7, 120 6 of 20

where P is the power, r is the density of air, A is the rotor area, v1 is the initial speed of the air, and a is the interference factor, which is generally equal to 1/3. From this, it was possible to calculate the amount of energy that could be produced in a year, and the expected revenue for each turbine, based on the average energy market price. The costs were calculated considering the sum of the investment and operating cost items.

3.2.2. Landscape Assessment The search for an objective basis for assessing assets with a high information density—which means involving semantics (relationship between signifiers and signified) and syntax (relationship between signs through their internal semantics)—combined with an analysis of a vast area, means greatly simplifying which signifiers to consider in the overall intervisible surface, and weighting them according to their distances and the quality of the intervisible areas from a landscape value point of view. For this purpose two intervisible area measuring modes and three types of area have been taken into account:

1. the intervisible area, that is measured:

a. in absolute terms, to assess the potential impact and the incidental overlapping between areas at different intervisibility; b. in relative terms, reducing the entity of the visible area with a coefficient which takes into account the distance and the actually visible size of the turbines; 2. the considered intervisible area, that is articulated in:

a. total intervisible area; b. intervisible area comprised in the protected areas; and, c. intervisible area comprised in natural areas, that means characterized by non productive and non transformative land uses.

Also the intervisibility measurement has been weighted with an index related to the distance and visibility conditions, meaning the portion of the turbine that is visible from each cell (40 × 40 m) of the intervisible surface.

3.2.3. Combination of the Two Valuations To make the measures of economic convenience comparable to those of the landscape quality, the results of the two analyses, as obtained in the various units of measurement, were normalized on the basis of a standard scale, associating the score with the minimum/maximum value of each performance to the minimum/maximum value of the standard scale, and calculating the intermediate values based on a linear proportionality between minimum and maximum. Then, each of the obtained score is multiplied by a weighting factor that takes into account the relative importance of each of the mentioned criteria compared to the others. Now, it is possible:

1. to aggregate the weighted scores in one general score allowing us to get a ranking of all the plant areas from the points of view of all the criteria; and, 2. to aggregate the weighted scores in two partial scores allowing us to characterize each plant area from the point of view of “energy” (economic criteria) and “information” (landscape criteria); this double characterization can be used to represent the “Pareto frontier”, that is the trade-off between energy and information. Land 2018, 7, 120 7 of 20 Land 2018, 7, x FOR PEER REVIEW 7 of 20

3.3.3.3. Syntactic Model. Decision ProfilesProfiles and Prescriptive ToolsTools The syntacticsyntactic model, which was aimed at providingproviding the rulesrules forfor thethe optimaloptimal layoutlayout choicechoice ofof thethe windwind farmfarm policies at the provincial scale, consists of a decision-making process involving severalseveral decision-makersdecision-makers includingincluding a a group group of of experts experts that that supports supports the choicesthe choices of local of local stakeholders. stakeholders. The latter The arelatter guided are guided according according to an iterative to an relationaliterative relational approach, approach, aimed at identifying aimed at diversifiedidentifying preferencesdiversified structures,preferences capable structures, of eliciting capable a shared of eliciting system a of shared preferences system aimed of preferences at mediating aimed between at themediating reasons ofbetween transformation the reasons and of those transformation of conservation and those (Figure of4 conservation). (Figure 4).

Information 0.7

0.6 5 7

0.5 4 3

Decision 0.4

Rules 0.3

2 step 1 0.2 8 1 6 9 0.1

0 0 0.2 0.4 0.6 0.8

Energy DM Expert Group DM group definition DM Local Stakeholders

Conservative Transformative approach approach information oriented energy oriented

Decision Rules Step 2

Information 0.7

0.6 4 7 0.5 5

0.4 3 0.3 1 8

0.2 2 6 0.1 9

0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Energy Figure 4. General flowflow chart of the decision making process.process.

The decisiondecision processes help DMs to convergeconverge on aa choicechoice ofof actionsactions toto implementimplement energyenergy andand territorialterritorial policiespolicies whenwhen environmentalenvironmental urgencyurgency andand thethe landscapelandscape valuesvalues areare inin structuralstructural conflict.conflict. The process of identifying the preferred structures of the territorial players may be conducted by implementing a suitable decision-making model supported by a specific algorithm. Land 2018, 7, x FOR PEER REVIEW 8 of 20

Among the algorithms developed to aid decisions that lend themselves to identifying decision preference structures, we selected the Interactive Multi-objective Optimization-Dominance-Based Rough Set Approach (IMO-DRSA) [26,27]. IMO-DRSA is an algorithm that develops through an interactive process, and it can be Land 2018characterised, 7, 120 by several steps, of which two can be considered the main phases (Figure 5). In the first 8 of 20 phase, a sample of solutions is deduced from a Pareto optimal set (or from an approximation thereof) generated in a pre-analysis phase on the basis of the preference structure of an expert group. The process of identifying the preferred structures of the territorial players may be conducted by In the second, the Decision Maker (DM) is asked to choose acceptable solutions from the example implementinggenerated. a It suitable is possible decision-making to extract an initial model preference supported structure by from a specific this information, algorithm. which will be Amongexpressed the using algorithms the typical developed syntax of tothe aid DRSA decisions algorithm that family, lend themselvesi.e., in terms of to decision identifying rules decisionof preferencethe type structures, “if…, then we…” selected[28–30]. The the rule Interactive set generated Multi-objective in this way defines Optimization-Dominance-Based some new constraints that Roughexpress Set Approach the preferences (IMO-DRSA) of the DMs, [26 which,27]. can be added to the original constraints of the problem in IMO-DRSAorder to identify is anthe algorithmsolutions that that are of develops no interest through in the Pareto an interactiveoptimal set generated process, initially. and it At can be characterisedthis point, by having several refined steps, the of information which two level, can be it is considered possible to thegenerate main a phases new sample (Figure of solutions5). In the first to submit to the DM, who in turn, is asked to express an opinion on the new analysis results. The phase, a sample of solutions is deduced from a Pareto optimal set (or from an approximation thereof) interaction continues until the decision rule sample generated represents the preference structure of generatedthe DM, ina who pre-analysis has been able phase to provide on the feedback basis of during the preference the iteration structure process. of an expert group.

FigureFigure 5. The 5. The Interactive Interactive Multi-objective Multi-objective Optimization-Dominance-Based Optimization-Dominance-Based Rough Set Rough Approach Set (IMO- Approach (IMO-DRSA)DRSA) flowchart. flowchart.

In theTherefore, second, the the Decision decision Makermodel and (DM) IMO-DRSA is asked toalgo chooserithm acceptableare able to solutionsidentify the from preference the example structure of the DM, starting from a successive process of exploration of the Pareto optimal set. The generated. It is possible to extract an initial preference structure from this information, which will be methodology offers a simple language for both inputs and outputs, which is perceived as being expressed using the typical syntax of the DRSA algorithm family, i.e., in terms of decision rules of the natural, being that of decision-making rules (Figure 6). type “if ...Specifically,, then ... ”[in 28the– 30case]. Thebeing rule studied, set generated the proposed in thismethodology way defines can i somedentity new the constraintswind farm that expressthat the the preferences DM perceives of as the being DMs, the whichbest out can of thos be addede analysed, to the in the original sense that constraints is explained of theabove. problem in orderThe to preference identify structure the solutions identified that is aredefined of no on interest the basis in of thea relationship Pareto optimal system that set generatedrepresents the initially. At thisway point in ,which having the refined DM decides, the information and is therefore level, char it isacterised possible by tothe generate correlation a new between sample a decision of solutions to submitand the to thefactors DM, (criteria) who inthat turn, led to is it. asked to express an opinion on the new analysis results. The interactionIn continues the previous until case, the the decision decision model rule sample (Figure generated6) is characterised represents by: a) five the condition preference criteria— structure of total intervisible area, plant visibility in the restricted area, overlapping intervisible area, restricted the DM, who has been able to provide feedback during the iteration process. intervisible area, NPV—and two decision criteria—“no”, meaning the plant must not be Therefore,implemented, the “yes”, decision meaning model the plant and may IMO-DRSA be implemented. algorithm are able to identify the preference structure of the DM, starting from a successive process of exploration of the Pareto optimal set. The methodology offers a simple language for both inputs and outputs, which is perceived as being natural, being that of decision-making rules (Figure6). Specifically, in the case being studied, the proposed methodology can identity the wind farm that the DM perceives as being the best out of those analysed, in the sense that is explained above. The preference structure identified is defined on the basis of a relationship system that represents the way in which the DM decides, and is therefore characterised by the correlation between a decision and the factors (criteria) that led to it. In the previous case, the decision model (Figure6) is characterised by: a) five condition criteria—total intervisible area, plant visibility in the restricted area, overlapping intervisible area, restricted intervisible area, NPV—and two decision criteria—“no”, meaning the plant must not be implemented, “yes”, meaning the plant may be implemented. Land 2018, 7, x FOR PEER REVIEW 9 of 20 Land 2018, 7, 120 9 of 20

Land 2018, 7, x FOR PEER REVIEW The windfarms 9 of 20

WindThe windfarmsfarm 1 Wind farm 2 Wind farm 3 WindWind farm farm 4 1 Wind….. farm 2 WindWind farm farm 9 3 Wind farm 4 Visible area ….. Wind farm 9 Visible in bound area The decision problem to support the wind farms Overlap areas planning Visible area Windfarm land Bound area Visible in bound area No Yes NPV ConditionThe decision criteria problem to supportDecision the criterion wind farms Overlap areas planning Windfarm land Bound area No Yes NPV Condition criteria Decision criterion Figure 6. Decision-making problem to support planning the wind farm system. Figure 6. Decision-making problem to support planning the wind farm system. 4. Results and DiscussionFigure 6. Decision-making problem to support planning the wind farm system. 4. Results and Discussion 4.1.4. Descriptive/SelectiveResults and Discussion Model Results 4.1. Descriptive/Selective Model Results 4.1.This Descriptive/Selective stage provides the Model early Results layout for the location of the wind farm over the whole territory of each province.This stage The provides subsequent the earlyevaluations layout forwill the be locationable to select of the the wind most farm appropriate over the profile whole territoryon the basisof each ofThis consolidated province. stage provides The procedure subsequent the early consistent layout evaluations forwith the th will locatie scopes beon able of of the tothe select wind energy-environmental thefarm most over appropriate the whole policy territory profile to be on of implemented.theeach basis province. of consolidated The subsequent procedure evaluations consistent will with be ab thele scopes to select of the most energy-environmental appropriate profile policy on the to bebasisAs implemented. offirst, consolidated the available procedure areas were consistent identified with exclud the scopesing from of thethe totalenergy-environmental territorial area the policyrestricted to be ones.implemented. FromAs first, the the point available of view areas of the were extent identified of suit excludingable areas from the thetwo total territories territorial differ area significantly. the restricted Basedones. onAs From the first, spatial the the point available analyses of view areas in of GIS the were and extent identified by ofintegr suitable atingexclud areas theing data the from twoof thethe territories totaltwo PTPRsterritorial differ and significantly. area the indicationsthe restricted Based ofon ones.the the Regional From spatial the analysesAssessments point of in view GIS to and theof the byTerritory integratingextent andof suit Cu theableltural data areas ofHeritage the the two two [7], PTPRs territoriesin the and Syracusan the differ indications significantly. area, nine of the areasRegionalBased (Figure on the Assessments 7) spatial were analysesselected to the in Territorywhile GIS andin Enna and by integr Cultural territoryating Heritage 31the (Figure data [ 7of], the8), in thewhichtwo Syracusan PTPRs denotes and area, the indicationsdifferent nine areas density(Figureof the ofRegional7 )landscape were selected Assessments values while recognized to in the Enna Territory at territory the institutional and 31 (FigureCultural level.8), Heritage which denotes [7], in the the Syracusan different density area, nine of landscapeareas (Figure values 7) were recognized selected at thewhile institutional in Enna te level.rritory 31 (Figure 8), which denotes the different density of landscape values recognized at the institutional level.

Key Wind farms Municipal boundary Key Built up areas buffer 5 km Wind farms Built up areas Municipal boundary CIS or SPZ Built up areas buffer 5 km ArchaeologicalBuilt up areas sites CoastalCIS orrestricted SPZ areas Law 1497 restricted areas Archaeological sites River restricted areas Coastal restricted areas 0 5 10 km Archaeological restricted areas Law 1497 restricted areas Lake areas 010,0005,000 River restricted areas Kilometers Natural reserves 0 5 10 km Archaeological restricted areas Lake areas 010,0005,000 Figure 7. Suitable areas inKilometers the Province of SiracusaNatural (our reserves GIS calculations). Figure 7. Suitable areas in the Province of Siracusa (our GIS calculations).

Figure 7. Suitable areas in the Province of Siracusa (our GIS calculations). Land 2018, 7, 120 10 of 20 Land 2018, 7, x FOR PEER REVIEW 10 of 20

FigureFigure 8.8.Suitable Suitable areasareas ininthe theProvince Provinceof ofEnna Enna (our (our GIS GIS elaboration). elaboration). ((aa)) HydrographicHydrographic basinsbasins andand anemometry.anemometry. ((bb)) Land Land uses uses and and bond bond areas. areas. ((cc)) GeneralGeneral schemescheme ofof thethe plantplant areasareas beforebefore consideringconsidering built-upbuilt-up areasareas bufferbuffer zone.zone. ((dd)) AllowedAllowed plant plant areas. areas.

The descriptive/selective model is intended to compare the production potential of the plants The descriptive/selective model is intended to compare the production potential of the plants withwith theirtheir impactimpact onon thethe landscapelandscape inin thethe twotwo territorialterritorial contextscontexts beingbeing examined.examined. ForFor thisthis reason,reason, thethe Piani Paesaggistici Territoriali descriptiondescription refersrefers toto thethe relativerelative ProvincialProvincial TerritorialTerritorial LandscapeLandscape PlansPlans ((Piani Paesaggistici Territoriali Provinciali Provinciali—PPTP)—PPTP) [[5,6];5,6]; thesethese bothboth havehave vastvast areasareas thatthat areare stillstill scarcelyscarcely urbanised,urbanised, andand thatthat areare thereforetherefore especiallyespecially sensitive sensitive to to the the intrusion intrusion of of abstract abstract and and atypical atypical objects. objects. FromFrom thethe pointpoint ofof viewview ofof theirtheir suitabilitysuitability forfor windwind energyenergy installations,installations, thethe territoryterritory ofof EnnaEnna sufferssuffers fromfrom aa centralcentral locationlocation andand itsits surfacesurface roughness,roughness, whichwhich attenuatesattenuates thethe currents,currents, whilewhile thethe territoryterritory ofof SiracusaSiracusa benefitsbenefits fromfrom aa mountainmountain range,range, thatthat ofof thethe HybleanHyblean Plateau,Plateau, whichwhich helpshelps toto maintainmaintain the the wind wind speed speed even even at at low low altitudes, altitudes, as as the the anemometric anemometric measurements measuremen confirmts confirm (see (see Table Table1). 1). The suitable areas in the two territories also differ significantly in size. Based on spatial analysis in GIS,The and suitable integrating areas in data the fromtwo territories the two Regional also differ Territorial significantly Landscape in size. Based Plans on (Piano spatial Territoriale analysis Paesisticoin GIS, and Regionale integrating—PTPR), data together from the with two information Regional fromTerritorial the Regional Landscape Department Plans (Piano of the Territoriale Territory andPaesistico Cultural Regionale Heritage—PTPR), [31], the together territory with of Siracusainformation can from provide the nineRegional areas, Department against 31 in of Enna the Territory despite theand considerable Cultural Heritage difference [31], in the size territory (see Figures of Siracusa1 and2 ),can which provide reflects nine the areas, different against density 31 in of Enna landscape despite valuesthe considerable that were recognized difference atin an size institutional (see Figure level.s 1 and 2), which reflects the different density of landscape values that were recognized at an institutional level.

Land 2018, 7, 120 11 of 20

Table 1. Minimum, average and maximum anemometric measurements in the two provinces (at an altitude of 10 m above ground level (http://www.sias.regione.sicilia.it/frameset_dati.htm January–December 2015).

m/s Province of Siracusa min avg max Siracusa - - - Lentini 9.0 11.5 13.4 Pachino 8.4 10.1 12.0 Palazzolo 10.4 12.5 14.2 Augusta 10.4 12.5 14.2 Province of Enna min avg max Aidone 0.5 1.1 1.8 Enna 1.9 3.1 4.0 Nicosia 1.6 2.1 2.7 Piazza Armerina 0.8 2.7 4.9 Agira 2.7 3.6 4.3 Calascibetta 2.3 3.0 4.9

4.2. Semantic Model Results Even the semantic model is divided into two main stages, the first aimed at economic evaluations, the second at the landscape ones.

4.2.1. Economic Valuation Results As above mentioned, a Discounted Cash Flow Analysis was performed in order to calculate the profitability of the investments to be implemented in the selected areas, and to compare the potential of the two territories; based on the profitability of each area, the ones unprofitable have been excluded. We assumed a 2.5% discount rate and a 20 years life time of the turbines, over which the investment has been considered unprofitable from the point of view of the Payback Period. Table2 exemplifies and summarizes the elements for the Discounted Cash Flow Analysis (DCFA) analysis of five of the nine plants hypothesized in the province of Siracusa.

Table 2. Elements of the cost/revenue analysis applied to the wind farm investments—five of the nine plants considered in the province of Siracusa (our processing).

Siracusa–Plant Id 1 2 3 4 5 Investment earth moving (1000 EUR/turb) 32 32 32 32 32 areas (EUR/m2) 6.25 6.25 6.25 6.25 6.25 turbines (EUR/kW) 1.175 1.175 1.175 1.175 1.175 machine cabins and housing (EUR) 4.44 3.15 3.79 3.79 4.44 contextual works (EUR) 51.6 44.88 48.25 48.25 51.63 environmental feasibility report (EUR) 14.6 7.86 11.24 11.24 14.62 preliminary investigation (EUR) 7.31 3.93 5.62 5.62 7.31 specialist workers and supervision (EUR) 120 79.95 99.92 99.92 119.9 founding of companies and insurance (EUR) 28.2 16.66 22.44 22.44 28.22 total investment cost (1000 EUR) 4.222 4.013 4.118 4.118 4.222 total inv cost/kW (EUR) 1.407 1.338 1.373 1.373 1.407 Annual operating costs site + royalties (EUR) 12.5 9.5 11 11 12.5 Maintenance + services + management (EUR) 7.54 7.54 7.54 7.54 7.54 total operating costs (1000 EUR) 60 51 56 56 60 discounted operating costs (1000 EUR) 937 797 867 867 937 Land 2018, 7, 120 12 of 20

Table 2. Cont.

Siracusa–Plant Id 1 2 3 4 5 Total costs total cost/turb (1000 EUR) 5.159 4.810 4.985 4.985 5.159 total cost/kW (EUR/kW) 1.720 1.603 1.662 1.662 1.720 total cost (1,000,000 EUR) 62 38 40 25 41 Energy Wind speed (m/s) 9.1 10 12 12.5 12.6 Productibility (MWh) 3018 4401 9126 10,745 11,093 Revenues unit revenues (EUR/kWh) 0.13 0.13 0.13 0.13 0.13 total revenues (1000 EUR/y) 345 503 1043.10 1228.10 1267.90 Results Gross Present Value (1,000,000 EUR) 64.5 62.7 130.1 95.7 158.1 Net Present Value (1,000,000 EUR) 4.7 22.9 90.2 70.8 118.2 External Rate of Return 7.90% 57.30% 226.20% 284.10% 296.50% Pay Back Period (y) 14.5 9.9 4.8 4.1 3.9 Internal Rate of Return 3.30% 7.90% 20.40% 24.30% 25.10%

The different anemometric conditions in the two provinces have led to report the results of the DCFA in different ways, as the economic results for Siracusa were positive for all nine farms, while only seven plants gave positive results in the territory of Enna; in particular, from the point of view of the indicators highlighted in grey shade in Table3.

Table 3. Results of the DCFA applied to the wind farm investments (31 plants in the province of Enna): GPV—Gross Present Value; NPV—Net Present Value; ERR—External Rate of Return; PBP—Pay Back Period; IRR—Internal Rate of Return (our processing).

ENNA Total Cost GPV NPV ERR PbP IRR Plants Id 1 EUR 77,390,711 EUR 89,149,371 EUR 11,758,660 15.2% 14 4% 2 EUR 72,149,894 EUR 115,466,566 EUR 43,316,672 60.0% 10 8% 3 EUR 74,702,803 EUR 115,466,566 EUR 40,763,764 54.6% 10 8% 4 EUR 74,770,303 EUR 57,821,446 EUR 16,948,857 −22.7% 20 0% 5 EUR 77,323,211 EUR 72,858,791 EUR 4,464,420 −5.8% 17 2% 6 EUR 74,702,803 EUR 72,858,791 EUR 1,844,012 −2.5% 16 2% 7 EUR 74,702,803 EUR 68,992,252 EUR 5,710,550 −7.6% 17 2% 8 EUR 72,149,894 EUR 62,485,485 EUR 9,664,409 −13.4% 18 1% 9 EUR 74,702,803 EUR 115,466,566 EUR 40,763,764 54.6% 10 8% 10 EUR 74,770,303 EUR 92,570,574 EUR 17,800,271 23.8% 13 5% 11 EUR 74,770,303 EUR 80,749,526 EUR 5,979,223 8.0% 14 3% 12 EUR 74,702,803 EUR 59,135,730 EUR 15,567,072 −20.8% 20 0% 13 EUR 72,082,394 EUR 66,151,418 EUR 5,930,977 −8.2% 17 2% 14 EUR 74,770,303 EUR 14,085,813 EUR 60,684,489 −81.2% 83 −11% 15 EUR 74,736,553 EUR 14,085,813 EUR 60,650,739 −81.2% 83 −11% 16 EUR 72,149,894 EUR 66,151,418 EUR 5,998,477 −8.3% 17 2% 17 EUR 74,770,303 EUR 74,348,367 EUR 421,935 −0.6% 16 2% 18 EUR 74,702,803 EUR 14,085,813 EUR 60,616,989 −81.1% 83 −11% 19 EUR 74,702,803 EUR 48,123,316 EUR 26,579,486 −35.6% 24 −2% 20 EUR 72,082,394 EUR 40,744,650 EUR 31,337,744 −43.5% 28 −3% 21 EUR 72,149,894 EUR 14,085,813 EUR 58,064,081 −80.5% 80 −11% 22 EUR 74,702,803 EUR 50,938,280 EUR 23,764,522 −31.8% 23 −1% 23 EUR 72,082,394 EUR 70,364,730 EUR 1,717,665 −2.4% 16 2% 24 EUR 74,700,303 EUR 32,713,553 EUR 42,056,750 −56.2% 36 −5% 25 EUR 74,702,803 EUR 29,665,027 EUR 45,037,776 −60.3% 39 −6% 26 EUR 72,082,394 EUR 84,855,460 EUR 12,773,065 17.7% 13 4% 27 EUR 74,702,803 EUR 37,356,438 EUR 37,346,365 −50.0% 31 −4% 28 EUR 74,702,803 EUR 29,665,027 EUR 45,037,776 −60.3% 39 −6% 29 EUR 74,770,303 EUR 16,784,712 EUR 57,985,591 −77.6% 69 −10% 30 EUR 74,702,803 EUR 22,656,834 EUR 52,045,969 −69.7% 51 −8% 31 EUR 78,149,118 EUR 17,545,910 EUR 60,603,208 −77.5% 69 −10% LandLand 20182018, 7,,7 x, 120FOR PEER REVIEW 1313 of of 20 20

4.2.2. Qualitative Analysis Results 4.2.2. Qualitative Analysis Results In order to measure the extension and the quality of the landscape impact of the profitable plant areas, Ina qualitative order to measure analysis the was extension performed and aimed the quality at integrating of the landscape the sole impacteconomic of theresults profitable with landscapeplant areas, issues, a qualitative assuming analysis that, waslikely, performed the most aimed profitable at integrating wind farm the sole installments economic resultsstand out with strongly,landscape so issues,irradiating assuming a remarkable that, likely, visual the impact, most profitable and vice wind versa farm the lower. installments stand out strongly, so irradiatingThe qualitative a remarkable analysis, visual based impact,on the intervisibility and vice versa of the each lower. of the plants, measures the impact that eachThe of qualitative the selected analysis, plants (i.e., based those on the consid intervisibilityered economically of each ofsignificant) the plants, has measures on the landscape the impact (seethat Figures each of 9 theand selected 10). plants (i.e., those considered economically significant) has on the landscape (seeThe Figures GIS9 elaborationsand 10). of Figures 9 and 10 show the extent of the intervisible area of each installment.The GIS The elaborations semantic of model Figures 9aims and 10to showindica thete extentthose of that the intervisiblemaximize areathe offunction each installment. of the “energy/informationThe semantic model value”, aims to as indicate articulated those in thatthe measures maximize of the economic function convenience of the “energy/information (dependent on thevalue”, energy as efficiency articulated of in the the plants measures according of economic to the anemometric convenience (dependentcharacteristics on theof their energy position) efficiency and of thethe landscape plants according quality to(which the anemometric is a function characteristics of the extension of theirof the position) area from and which the landscape the plants quality are visible).(which is a function of the extension of the area from which the plants are visible). TheThe aggregated aggregated scores scores calculated calculated for for each each of of the the plants plants allow allow to to form form a aranking, ranking, as as exemplified exemplified inin Figure Figure 66 only only with with reference reference to to the the province province of of Siracusa, Siracusa, as as shown shown in in Table Table 4,4, which which shows shows in in the the upperupper part part the the economic economic and and spatial spatial measures measures and and at at the the bottom bottom the the corresponding corresponding scores. scores.

FigureFigure 9. 9.CumulativeCumulative intervisibility intervisibility of the of theseven seven wind wind farms farms selected selected in the in province the province of Enna of Enna(our GIS (our calculations).GIS calculations). Land 2018, 7, 120 14 of 20 Land 2018, 7, x FOR PEERPEER REVIEWREVIEW 1414 of of 20 20

FigureFigure 10. 10.Partial Partial intervisibility intervisibility of of the the wind wind farmsfarms farms consideredconsidered considered inin thethe in provinceprovince the province ofof SiracusaSiracusa of Siracusa (our(our GISGIS (our GIScalculation).

TheThe radar radar diagram diagram of of Figure Figure 11 11 displays displays thethe qualitative qualitative comparisoncomparison comparison ofof the ofthe the ninenine nine suitablesuitable suitable plantplant plant areasareas identified identified in thein the territory territory of of Siracusa Siracusa measuring memeasuringasuring and andand comparing comparingcomparing theirtheir performancesperformances performances accordingaccording according to eachto criterion. each criterion.

tot visible area Identified sites tot visible area Information Identified sites 1.51.5 Information in Syracuse 1.2 in Syracuse 1.2 0.70.7 weigh.weigh. tottot visiblevisible province IRRIRR 1.1 1.0 areaarea 1.1 1.0 0.6 5 0.6 5 11 77 1 0.9 0.5 4 0.50.5 0.9 0.5 4 33 2 0.8 3 0.8 3 0.40.4 PbP 0.00.0 restr.restr. visib.visib. AreaArea 0.7 4 0.7 0.6 0.3 5 0.6 0.3 6 6 0.5 0.2 2 8 0.5 0.2 2 8 7 7 0.4 9 0.4 1 6 9 8 restr.restr. weigh.weigh. visib.visib. 0.1 1 6 8 ERR 0.3 0.1 areaarea 0.3 9 0.2 0 0.2 0 0 5 10 15 20 25 0 0.2 0.4 0.6 0.8 NPV 0 5 10 15 20 25 0 0.2 0.4 0.6 0.8 EnergyEnergy Figure 11. Assessment and comparison of the various plants considered; trade-off between Energy FigureFigure 11. 11.Assessment Assessment and and comparison comparison ofof thethe various pl plantsants considered; considered; trade-off trade-off between between Energy Energy and Information (The criteria in terms of energy are NPV—Net present value, ERR—External rate of andand Information Information (The (The criteria criteria in in terms terms ofof energyenergy are NPV—Net NPV—Net present present value, value, ERR—External ERR—External rate rateof of return, PbP—Pay Back Period, IRR—InIRR—Internalternal RateRate ofof Return;Return; TheThe critercriteriaia inin termsterms ofof informationinformation areare return, PbP—Pay Back Period, IRR—Internal Rate of Return; The criteria in terms of information are tot. visible—total visible area; weigh. visible area—warea—weightedeighted visiblevisible area;area; restr.restr. visiblevisible area—area— tot. visible—total visible area; weigh. visible area—weighted visible area; restr. visible area—restricted restricted visible area; weigh. restr.restr. visiblevisible area—weightedarea—weighted restrictedrestricted visiblevisible area).area). visible area; weigh. restr. visible area—weighted restricted visible area). Land 2018, 7, 120 15 of 20

Land 2018, 7, x FOR PEER REVIEW 15 of 20 Table 4. Quantitative and qualitative assessment terms of the nine plants considered in the province ofTable Siracusa. 4. Quantitative and qualitative assessment terms of the nine plants considered in the province of Siracusa. Information Energy Total Area (ha)Information Restricted Area EffectivenessEnergy Efficiency Total AreaTotal (ha) Weighted Restricted Restricted AreaWeighted Effectiveness Efficiency NPV (EUR PbP TotalVisible Visible Visible RestrictedWeighted ERR IRR Weighted Restricted NPVMillion) (EUR (Years)PbP VisibleArea Area Area VisibleRestricted Area ERR IRR Visible Area Visible Area Million) (Years) Measures Area Visible Area Measures 1 15.020 8.833 184 116 4.7 7.9% 14.5 3.3% 1 15.020 8.833 184 116 4.7 7.9% 14.5 3.3% 2 7.196 2.394 95 37 22.9 57.3% 9.9 7.9% 2 37.196 10.908 2.394 4.971 95 142 3763 90.2 22.9 226.2%57.3% 4.89.9 20.4%7.9% 3 410.908 19.634 4.971 2.790 142 458 6367 70.8 90.2 284.1%226.2% 4.14.8 24.3%20.4% 4 519.634 19.762 2.790 3.998 458 199 6734 118.2 70.8 296.5%284.1% 3.94.1 25.1%24.3% 5 619.762 8.637 3.998 1.885 199 217 3450 118.2 4.4 12.7%296.5% 13.83.9 3.8%25.1% 6 78.637 7.607 1.885 2.636 217 143 5055 92.5 4.4 309.6%12.7% 3.813.8 26.0%3.8% 7 87.607 4.456 2.636 925 143 40 55 8 20.0 92.5 57.3%309.6% 9.93.8 7.9%26.0% 8 94.456 4.418 925 390 40 55 85 4.420.0 17.7%57.3% 13.29.9 4.3%7.9% 9Assessments 4.418 390 55 5 4.4 17.7% 13.2 4.3% Assessments1 0.56 0.25 0.90 0.25 0.25 0.25 0.22 0.22 1 20.56 1.07 0.25 1.01 0.90 1.12 0.25 0.97 0.410.25 0.410.25 0.600.22 0.400.22 2 31.07 0.83 1.01 0.71 1.12 1.01 0.97 0.72 1.000.41 0.970.41 1.030.60 0.900.40 3 40.83 0.26 0.71 0.97 1.01 0.25 0.72 0.69 0.831.00 1.170.97 1.091.03 1.050.90 4 50.26 0.25 0.97 0.82 0.25 0.87 0.69 0.99 1.250.83 1.211.17 1.111.09 1.081.05 6 0.98 1.07 0.83 0.84 0.25 0.27 0.28 0.24 5 0.25 0.82 0.87 0.99 1.25 1.21 1.11 1.08 7 1.04 0.98 1.00 0.80 1.02 1.25 1.12 1.12 6 0.98 1.07 0.83 0.84 0.25 0.27 0.28 0.24 8 1.25 1.19 1.25 1.22 0.39 0.41 0.60 0.40 1.04 0.98 1.00 0.80 1.02 1.25 1.12 1.12 7 9 1.25 1.25 1.21 1.25 0.25 0.28 0.32 0.26 8 1.25 1.19 1.25 1.22 0.39 0.41 0.60 0.40 9 1.25 1.25 1.21 1.25 0.25 0.28 0.32 0.26 In order to verify the robustness of the ranking of the plants 25 weight systems have been implemented.In order Theto verify multi-linear the robustness diagram of ofthe Figure ranking5 displaysof the plants the variation25 weight of systems the aggregated have been score ofimplemented. the nine plant The areas multi-linear for each diagram of the 25of Figure weight 5 di systems,splays the showing variation that of the the aggregated effect of the score weight of is significantthe nine plant just forareas the for plants each numberof the 25 2,weight 4, and syst 9,ems, whose showing aggregated that the scores effect areof the similar, weight and, is as a significant just for the plants number 2, 4, and 9, whose aggregated scores are similar, and, as a consequence, whose position in the ranking is influenced by the weight system. The third graph of consequence, whose position in the ranking is influenced by the weight system. The third graph of Figure 11 displays a possible trade-off between energy and information: plants 3, 4, 5, and 7 have a low Figure 11 displays a possible trade-off between energy and information: plants 3, 4, 5, and 7 have a visuallow visual impact impact and a and scarce a scarce power-economic power-economic efficiency, efficiency, and and vice vice versa versa for for plants plants 2, 6,2, 8,6, and8, and 9; plant9; 1, althoughplant 1, barelyalthough profitable, barely profitable, have a high have visuala high impact.visual impact. Enna,Enna, on on the the other other hand,hand, becausebecause of of the the lesser lesser economic economic efficiency efficiency and and energy/environmental energy/environmental effectivenesseffectiveness ofof aa policypolicy thatthat af affectsfects substantial substantial areas areas of ofthe the terri territory,tory, lends lends itself itself to additional to additional verificationverification of of the the trade-offtrade-off betweenbetween quantitative quantitative and and qualitative qualitative goals. goals. Regarding Regarding all 31 all plants 31 plants considered,considered, we we compare compare thethe performances that that are are contrary contrary and and complementary, complementary, to the to search the search for a for a significantsignificant relationship relationship inin aa planningplanning sense, sense, with with poor poor results results in inthe the initial initial phase phase (Figure (Figure 12). 12).

2.00 2.00

1.50 1.50

1.00 1.00

0.50 0.50

0.00 0.00 0.00 0.50 1.00 1.50 2.00 0.00 0.50 1.00 1.50 2.00

2.00 2.50

2.00 1.50

1.50 1.00 1.00 0.50 0.50 0.00 0.00 0.00 0.50 1.00 1.50 2.00 0.00 0.50 1.00 1.50 2.00 ERR FigureFigure 12. ComplementarityComplementarity and and trade-offs trade-offs between between variables. variables. Land 2018,, 7,, 120x FOR PEER REVIEW 16 of 20

Therefore, we have defined clusters of plant areas with comparable sizes, intervisible areas, and economicTherefore, results, we and have we defined have only clusters compared of plant th arease External with comparable Rate of Return sizes, and intervisible average areas,weighted and economicintervisible results, areas, defining and we havethe significant only compared relationsh theips External in three Rate of the of four Return clusters and considered average weighted (Figure intervisible13). areas, defining the significant relationships in three of the four clusters considered (Figure 13).

2.00 2.00

1.50 1.50

1.00 1.00

0.50 0.50

0.00 0.00 0.00 0.50 1.00 1.50 2.00 0.00 0.50 1.00 1.50 2.00

2.00 2.00

1.50 1.50

1.00 1.00

0.50 0.50

0.00 0.00 0.00 0.50 1.00 1.50 2.00 0.00 0.50 1.00 1.50 2.00

Figure 13. Figure 13. Trade-off between variables on a reduced sample.sample. 4.3. Syntactic Model Results 4.3. Syntactic Model Results The results achieved so far, based on an articulated quantitative and qualitative evaluation model, provideThe programmaticresults achieved elements so far, concerning based on an individual articulated territorial quantitative and landscape and qualitative units, based evaluation on an argumentativemodel, provide rationality programmatic. To be ableelements to outline concerning an overall individual decision-making territorial andand planninglandscape framework, units, based it ison useful an argumentative to broaden therationality articulation. To ofbe theable evaluation to outline model an overall by involving decision-making the tools of and the discussingplanning rationalityframework,, as it explained is useful to in broaden section 3.3. the articulation of the evaluation model by involving the tools of the discussingAccording rationality to the proposed, as explained methodology, in section taking 3.3. as sample the case of Siracusa, in the first phase, According to the proposed methodology, taking as sample the case of Siracusa, in the first phase, decision-making rules are extracted from a decision-making model that was implemented to represent decision-making rules are extracted from a decision-making model that was implemented to the preference structure of a Decision Maker Expert Group (DMEG); then this structure is proposed to represent the preference structure of a Decision Maker Expert Group (DMEG); then this structure is the Decision Makers Local Stakeholders (DMLS) who was asked to declare his preferences (Table5). proposed to the Decision Makers Local Stakeholders (DMLS) who was asked to declare his preferences (Table 5). Table 5. Decision-making rules for the three approaches.

TableThe 5. IMO-DRSADecision-making Approach rules to for the the Decision three Problemapproaches. DMEG DMLS DMLS Rules The IMO-DRSA ConservativeApproach to approach the Decision Problem Transformative approach DMEG (Information oriented)DMLS (Energy oriented)DMLS Rules If 1,000,000 EUR < NPV Conservative approach Transformative approach If the visible area is less than If the visible area is less than < 1,500,000 EUR, then is 1 40 square(Information kilometers, then oriented)is 40 square kilometers, (Energy oriented)then is possible to realize the possibleIf the to visible realize thearea wind is less farm than 40possible If the to realize visible the area wind is farmless than If 1,000,000wind EUR farm < NPV < square kilometers, then is 40 square kilometers, then is 1 1,500,000If 500,000 EUR, EUR then < NPVis possible < If the visiblepossible in bound to realize area is the equal windIf the visiblepossible in bound to realize area is the equal wind 1,000,000 EUR, then is not 2 to realize the wind farm to 2 square kilometers, then is not to 2.5 square kilometers, then is not possible to realize the farm farm possible to realize the wind farm possible to realize the wind farm If 500,000wind EUR farm < NPV < If the visible in bound area is If the visible in bound area is 1,000,000 EUR, then is not equal to 2 square kilometers, equal to 2.5 square kilometers, 2 If 500,000 EUR < NPV < If 500,000 EUR < NPV < 3 possible to realize the wind 1,000,000the EUR,n is notthen possibleis not possible to realize1,000,00 then EUR, is notthen possibleis not possible to realize farm to realize the windwind farm farm to realizethe windwind farm farm If 500,000 EUR < NPV < If 500,000 EUR < NPV < 1,000,000 EUR, then is not 1,000,00 EUR, then is not 3 In the second phase, with reference to the inputs from the DMEG, the results of which are shown possible to realize the wind possible to realize the wind below (Figures 12 and 13), it was possible to identify the main approaches (Table6): one is more farm farm Land 2018, 7, 120 17 of 20 conservative, information oriented, landscape oriented, and more sensitive to the presence of restricted areas (where there were high acceptability thresholds for the intervisible areas and visible plants in restricted areas), and another that intended to identify configurations that make it possible to achieve the best economic performance, the transformative approach energy oriented (where the acceptability threshold for the intervisible area and plant visibility in restricted areas, and those in which even the interaction of overlapping intervisible areas, is considered acceptable under the following conditions: the intervisible areas must not overlap by more than 10% of the largest one).

Table 6. Wind farm choices in relation to the three approaches.

The Decision Process to Support the Wind Farms Planning in Syracusa’s Territory DMEG DMLS DMLS Final DMLS Conservative approach Transformative approach Conservative/Transformative approach Wind Farms (Information oriented) (Energy oriented) (Energy/information oriented) E1 No No No No E2 No No No No E3 Yes No No No E4 No No No No E5 Yes Yes Yes Yes E6 Yes No Yes Yes E7 Yes Yes Yes Yes E8 No No Yes No E9 Yes No Yes Yes

5. Conclusions The incessant technological progress and the progressive deterioration of natural structures, together with the exponential growth of human needs, are giving rise to a rapid environmental drift to which the economic, social, and political ones inevitably associate. There is no doubt that economic and financial crises and large migratory movements are linked to the environmental question; the convergence of these two circumstances pushes communities to adopt closed attitudes towards the great geopolitical institutions and suspect of supranational aggregations, with a great risk for democracy and the progress of ideas and consciences. The mission of the Science of evaluations, an operational discipline that is closely linked to the issues of distributive equity of the transformations of local environmental and territorial contexts, is to integrate a multiplicity of evaluation tools within a vision inspired by the combination of “ethics of landscape “and “ aesthetics of economy”. These two aspects are strongly intertwined with each other due to some circumstances not easy to cope with:

- the first, more general, concerns the need to free ourselves from the fossil supply chain, given the extensive and uncontrollable ramification in space and time of its catastrophic effects, not only environmental, but also and above all socio-economic; and, - the second, more directly connected with the shape of the territory, is given by the consumption of soil that every form of “self-sufficient solidarity”—that is the self-production of the energy that is consumed—can provoke, and the consequent impacts.

The exploitation of the wind source is one of the most peculiar cases of this issue, precisely because of the high economic efficiency of the higher wind turbines, and especially the difficulty in ascertaining the visual impact as a negative value, given that the rules of “visual pollution” are not unique, like those of water, atmospheric, acoustic, olfactory pollution, etc.; then, the geography of value is the framework of the fair form of energy. The case presented in this study is an opportunity to explore the different energy-economic potential of two Sicilian territorial contexts—in many ways opposites—in the light of their formal resilience, with the aim of providing local administrations (regional and provincial) contents, objectives, and tools of the process of selection, evaluation, and planning of energy policies in sensitive landscapes. Land 2018, 7, 120 18 of 20

The contents are the evaluation terms with which investments in wind farms can be represented and compared in the “space of values”: the two general terms, according to the new approach to value and evaluations proposed by Francesco Rizzo are energy and information, the latter understood as a form of the territory, therefore with reference to the layout of the landscape. The main indicators of these value terms are: for energy the economic measures that make the investments profitable, then the results of the Discounted Cash Flow Analysis; for the information, the visibility of the plants was considered taking into account: the merit of the areas affected by intervisibility; the distance of these areas from the wind farms; the possible overlapping of the intervisible areas. To do this, the spatial analysis functions in the GIS environment were used. The objectives are the shared decision rules that are based on robust assessments. To this end, once the areas suitable for wind farms have been identified, and the economic value of the related investments and the visual impact have been calculated, a decision model based on the discussion rationality, i.e., the construction of decision rules, has been elicited: - by two groups of decision makers, experts and administrators/stakeholders; and, - by considering two different approaches, one more conservative, the other more transformative. The tools that we used in this proposal are: the GIS spatial analysis for the selection of the suitable areas according to the restricted areas, the road network and the built-up areas; the Discounted Cash Flow Analysis taking into account the revenues depending on the different producibility of each plant areas due to the foreseen windiness; the IMO-DRSA relational pattern, a Decision Making path aimed at the construction of the rules that allow for the administrators to implement policies based on shared decisions. The province of Enna, covering the central area of Sicily, due to its position and to the roughness of its mountainous territory that dampens the currents, is less suitable for the implementation of wind farms. As a consequence, of the 31 suitable areas, only seven were actually profitable, those that were placed on the highest mountain ranges, and therefore with greater visual impact, as shown by the GIS calculations and displayed by the intervisibilty map. The province of Siracusa, due to its position and the large water front, in addition to its orography, takes advantage of better anemomentric conditions that make investments in wind farms much more profitable. On the other hand, a greater density of cultural and landscape values reduces the suitable areas and the possibilities of exploitation of the wind resource. It follows that the territory of the province of Siracusa is more able to concentrate the potential of wind energy and reveals the context of greater economic-energy efficiency. Some quantitative results may be useful to outline a geography of values for each of the two territories identified by the proposed axiological filter: the calculated total energy potential, assuming 66 for Siracusa and 140 for Enna 3 MW generators, which corresponds to a productibility, respectively, of 61 and 16 MW, with a notable efficiency gap with regard to the economic result, given that the unit cost is around 1700 per kW. The landscape cost is also very different, being 976 (for Siracusa) and 1682 (for Enna) km2. Consequently, extending this tool to the whole regional territory would make it possible to define the intervention priorities consistently, and to bring analyses together where effectively necessary, making the whole information and organisation system more efficient, according to a single multi-level method [32,33]. The proposed tool supports a “value geography policy”, which connects the horizontal dimension of extensive knowledge with the vertical dimension of detailed analytical assessment to consolidate a summary vision that is based on value judgments. These results have suggested some in-depth analysis, useful for the economic-territorial planning, aimed at defining the trade-off relations between performances related to energy and information, and subsequently, through the application of a structured decision-making consultation process, a set of decision rules that mediate between a conservative, information-oriented, and a transformative, energy-oriented approach. The different energy-information profiles of the two provinces constitute the topical example of non-dominant alternatives whose choice first requires a robust model of analysis and evaluation Land 2018, 7, 120 19 of 20 and then a decision-making process whose level of participation is guaranteed by the elicitation of decision-making rules inspired by forms of discussion rationality.

Author Contributions: Conceptualization, S.G., F.N., F.G., M.R.T.; Data curation, F.G., M.R.T.; Formal analysis, S.G., F.N., F.G., M.R.T.; Funding acquisition, F.N.; Investigation, S.G., F.G., M.R.T.; Methodology, S.G., F.N., F.G., M.R.T.; Writing—original draft, S.G; Writing—review & editing, S.G., F.N., M.R.T. Conflicts of Interest: The authors declare no conflicts of interest.

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