Assessing the Economic Aspects of Landscape

Abstract

There are many economic aspects associated with landscape. Firstly, landscape is an “externality”, as the economic activities involving the use and transformation of landscape have different effects and repercussions on the same; secondly, landscape, especially in modern society, is seen more and more as a limited resource, and is therefore perceived as an “economic good”. In consideration of these assumptions, the current chapter will examine the main indicators used in literature to assess the economic aspects of landscape, with an interpretation on the basis of two major approaches to analysis: the “economic value” of landscape and the “economic strength” of landscape. Finally we will propose a set of indicators based on the DPSIR model on two different scales for monitoring macro transformations (regional scale) and the following in-depth study (local scale).

8.1 Principles and Definitions

There are many economic aspects associated with landscape. The economic activities related to the use and transformation of landscape have various effects and repercussions on the same; according to the literature in the field of economic analysis this is tantamount to saying that landscape is a (positive or negative) externality (Marangon and Tempesta 2008). In general terms, externalities are defined on the basis of the effects (favourable or unfavourable) on the production or consumption of one person by the production or consumption of another, without there being any kind of monetary transaction between the two to balance the costs or benefits of these effects.

Furthermore, landscape, especially in modern society, is seen all the more as a limited resource. From the point of view of economic analysis, this is the same as saying that landscape can be considered an “economic good”, in other words a good available in an insufficient quantity to meet requirements for the same, and for which there is a problem of efficient allocation of resources, guaranteed or not as the case may be by the spontaneous actions of the market (Santos 1998).

In consideration of said characterization, the use of evaluation tools to estimate the value of landscape can be explained on the basis of two main themes. First and foremost we must have tools to establish and assess the foreseeable benefits of certain actions involving the use and transformation of landscape. Secondly, techniques must be established for the assessment of the effectiveness and efficiency of public expenditure for interventions on landscape.Therefore landscape assessment can be translated into economic indicators used to draw up policies for the protection and requalification of landscape.

Indicators have only recently been used in the assessment of the economic aspects of landscape, and are still subject to development. Available publications on the subject indicate two main approaches in the study of economic landscape indicators . The first, more experimental approach, refers to the so-called “economic value” of landscape (Marangon and Tempesta 2008; Marone 2007). According to this approach, the value of landscape is generally established by the so-called existence value, theoretically unrelated to the benefit each person could obtain from a resource, substantially a value closely associated with the many functions it may have for man. In this perspective, landscape has a historical, cultural, recreational, panoramic and aesthetic value; it represents a value for the spirit, for its contribution to biodiversity and ecosystems, security and stability, the production of goods, and employment (Reho 2007).

These aspects/functions of landscape refer to various parties with a vested interest: for farmers (in the case of farmland) and rural communities it is a place to live and work; for society it is a recreational place; but landscape also provides specific environmental services associated with maintaining biodiversity and ecosystems, etc., of interest for generations both present and future.

With the first approach, there are two types of landscape demands (and components of the value).The first demand derives from the tendency of people to try and pass part of their time in more pleasant or more interesting environments from an aesthetic and perceptive point of view. Therefore, the quality of landscape influences the real estate market and recreational behaviour, while a second type of landscape demand is related to the need to protect cultural heritage in its various forms.

A second, more consolidated, approach, that has been called “economic strenght ” (Nordregio 2000), establishes a connection between the value of landscape and the contribution of the same to the economic system of reference. Therefore, this involves assessing landscape on the basis of the effects that utilization and transformation have on the economic system. These effects are connected, for example, with an increase in tourist flow deriving from the implementation of policies for landscape development of a site or job losses in the agricultural sector as a result of financial measures implemented to support agricultural production of specific value for landscape.

8.2 A Review of Economic Landscape Indicators

The search for specific indicators able to represent the economic aspects of landscape is used in a recent and quite expertimental literature.

The OECD (2001b), as part of its activities associated with the assessment of agricultural policies, lists a series of indicators for landscape assessment, including also some indicators based on economic value.

These indicators were used and studied in depth in a recent Italian study (Marangon and Tempesta 2008), with a precise classification of economic indicators, applicable assessment techniques, and references to specific studies of the past.

In particular, in the authors’ opinion, the value of landscape is attributable to the benefits produced by the same. In this sense, the categories of benefits that landscape can produce are associated with the following aspects:

• 1. Benefits from direct non-extractive use, in other words when a person uses an area with an attractive landscape for recreational purposes. To establish this value, the importance of landscape essentially depends on the type of recreational activity;

• 2. Benefits from indirect use associated with owning a home in a certain area with an attractive landscape;

• 3. Benefits from the non-use of the landscape due to the conservation of historical-cultural heritage.

We must emphasise that the use of these indicators is dictated by the availability of very specific data and information, which can only be obtained through direct interviews and surveys examining the benefits produced by landscape from the point of view of potential users (inhabitants or tourists for example). With this approach, the indicators relevant to the value of landscape can substantially be divided into monetary and non-monetary indicators.

For non-monetary indicators, according to Marangon and Tempesta (2008), the result in quantitative or qualitative terms depends on the criteria used in the various fields of interest (with reference to diversity, connectivity, etc. from an ecological point of view, visual quality, complexity, coherence, mystery, etc. from a perceptive point of view, etc.).

There are many publications on the question, from a variety of disciplines. The methods of assessment to which these criteria refer are divided into objective methods (indirect, historical for example) and subjective methods (direct, visual perception for example) by convention. The first are based on the opinions of experts in the assessment of material and formal aspects; the second are based on the level of satisfaction of the community of users in relation to the more intangible aspects of landscape (the identity, symbolic and cultural value, …). The use of these indicators lets us attribute a value to landscape to draw up policies, and comprehend the level of satisfaction in landscape and transformations by society (Tempesta 2006).

As for monetary indicators however, there are some methods that can provide an economic assessment of value for landscape (Stellin and Rosato 1998). These methods can be divided into two major categories, depending on whether they are based on the costs to bear for producing and maintaining the asset, or on the demand of the same asset.

Therefore, we have:

• Methods based on supply analysis (costs)

• Methods based on demand (benefits)

In other words, the monetary value of landscape refers to two main categories of indicators relevant to:

• the cost to maintain and develop certain landscapes;

• the willingness to pay to use a certain landscape, or accept compensation for not using the same.

The analyses of the cost/opportunities for alternative landscape and cultivation assets and the quantification of the costs necessary for the conservation of landscape (defensive expenditures) belong to the first group. The assessment of the benefits produced by the landscape, which can be calculated using methods based on the stated preferences (the willingness to pay to keep a certain landscape intact for example) or on revealed preferences (travel costs to use a certain landscape for example) belong to the second.

Table 8.1 contains a classification of the indicators available for the assessment of the economic value of landscape.

Table 8.1 Economic value of landscape: indicators for assessment. (Source: Marangon and Tempesta 2008)

Table 8.2 Results of the regression model. (Source: Verbič and Slabe-Erker 2009)

Despite the many difficulties involved in the application of calculations for the proposed indicators (monetary in particular), the same certainly provide a major contribution in assessing demand and supply for the landscape good. The use of these indicators can therefore be a useful support in the development of landscape policies, providing information on the importance attributed to the same by the local population, and also a trade-off between costs and benefits associated with the management of a certain landscape.

The approach used to establish landscape value based on the contribution of said landscape to the economic system of which it is part (“economic strenght ”) refers to more consolidated publications on the theme of assessment of the economic structure and performance for a certain area (Eser 1999; Nordregio 2000).

With this approach the indicators are used for the assessment of agro-environmental policies and refer to interscalar type applications ranging from a national level (assessment of economic performance in the agro-environmental sector of the various member states of the European Union) to a local level (assessment of the effects of financial measures to support single rural enterprises).

It must be said that, unlike the first approach, this approach does not explicitly refer to the theme of landscape, but rather to a series of policies and actions in the territory which envisage, amongst other things, also interventions for the protection and reclamation of landscape.

This approach is usually followed in Rural Development Programmes promoted by the European Union where the aim is to assess and test the effectiveness of public expenditure to reach planned goals.

The main references to this approach are the indicators of the PAIS project—Proposal on Agri-Environmental Indicators (Landsis et al. 2002) and the CMEF model (Common Monitoring Evaluation Framework), recently implemented by the European Commission (2006) to assess Rural Development Programmes.

In particular, the PAIS project proposes a set of economic type indicators to apply in the assessment of rural development at a European level. These are descriptive social-economic indicators concerning the quality of life; economic structure and performance; population and migration (Table 8.3).

Table 8.3 Themes of reference for PAIS project indicators

In the CMEF model however, there are a series of indicators that provide a quantitative figure on the contribution of landscape policies (agricultural policies in this case) for the overall economic requalification of the area in question.

The studies on indicators for the sustainable development of the agricultural sector (Wascher 2000; Waarts 2005; EEA 2005; MTT 2002; Van Heuckelom 2004), the cattle-farming sector (Wright et al. 1999) and the forestry sector (MCPFE 1998) also refer to this approach.

Finally, there are a series of studies on landscape assessment through multicriteria analysis, in which economic indicators are used with others for global landscape assessment (Gómez et al. 2003).

Box 8.1 Estimate of the Landscape Value Using the Contingent Valuation Method (Verbič and Slabe-Erker 2009)

The Contingent Valuation method was applied to estimate willingness-to-pay for the implementation of a plan for the development and conservation of the Volcji Potok landscape area in Slovenia. In particular, this is chiefly an agricultural area currently in a condition of degradation/abandonment, which the landscape plan would help preserve and reorganize, making the area more attractive for tourists.

The Contingent Valuation method was applied in various steps:

• 1. Data collection

  The sample used in the estimate consisted of 312 individuals, classified as inhabitants and tourists. The interviews held with the sample aimed to establish the willingness-to-pay (WTP) for the realisation of the development scenario of the area in question over the next 5 years.

• 2. Data elaboration

  The data collected was elaborated using a regression model. Table 8.2 shows the regression coefficients obtained; these coefficients indicate the contribution of the various elements in the model used to calculate the final WTP. As we can see, the most significant element in the formation of the WTP refers to the practical characteristics of the area such as, for example, the presence of cycle paths, footpaths and other features attractive for tourists (FUNCT variable). Furthermore, great importance is attributed to the conservation of natural and cultural heritage (CONSC variable).

The WTP value calculated with the regression model was corrected using more sophisticated estimates to obtain a final value indicating a willingness-to-pay per individual equal to 419 SIT/month/individual (equal to roughly 1.75 €/month/individual). Multiplying the figure obtained by the number of residents and tourists in the area (19,332) and calculating the value for one year, the result is a willingness-to-pay equal to 97.4 million SIT/year (roughly 406,000 €/year). Finally, the willingness-to-pay value for the development period of the plan (5 years) is equal to 486.8 million SIT (roughly 2 billion €).

8.2.2 Catalogue of Indicators

Below you will find a list of the main economic indicators used for the assessment of landscape in current publications, on the basis of the two approaches described above. The indicators have been organized in brief categories on the basis of the subject (Table 8.4).

Table 8.4 Indicators for assessing the economic aspects of landscape

8.3 Proposal for Economic Landscape Indicators

On the basis of the published indicators described above we will now propose a selection, which will later be studied in depth from the point of view of application.

For the selection of the indicators we decided to adopt some criteria for establishing the significance of the same, taking for granted that all the published indicators meet essential requirements for environmental indicators (see Sect. 2.1.1 of this report).

The criteria used to select the indicators refer to:

• Field of application: the criterion is used to measure the level of technical and operational difficulty and to calculate the indicator (holding ad hoc interviews, static elaborations, …), and to interpret the results;

• Completeness: the criterion indicates whether the indicator considers (from an economic point of view) the various aspects involved in the landscape system in a comprehensive way: not only agricultural structure, but also aspects associated with perception, tourism flows …;

• Specificity: the criterion establishes whether the indicator is essential or not in the economic characterization of landscape.

When selecting the indicators we chose to favour those characterised by completeness and high specificity; furthermore, we decided to consider indicators that can be used in both approaches.

The selection resulted in the following indicators (Table 8.5).

Table 8.5 Indicators proposed for the assessment of economic landscape aspects

Note that each of the indicators proposed corresponds to a specific scale of application. The scale is closely linked to the availability of source data for calculating the indicators, in order to obtain a legible result. In this way, two different systems of economic indicators are created: one for monitoring macro transformations (regional and provincial) and the other for studying the analyses in-depth (sub-provincial and local level).

Furthermore, as can be seen in the last column of Table 8.5, the proposed indicators guarantee coverage of all the DPSIR model categories.

8.3.1 Presentation of the Indicators Proposed

Below you will find an in-depth presentation of the indicators proposed (Tables 8.6, 8.9, 8.12, 8.13, 8.16, 8.17, 8.18, and 8.19), on the basis of the presentation table used for the study (Sect. 2.2.2). Where possible, the indicators have specific boxes to illustrate their application. The boxes contain some examples related to real cases where the different indicators have been calculated.

Table 8.6 Recreational benefits

Table 8.7 Results of the estimate with the initial regression model. (Source: Tempesta et al. 2002)

Table 8.8 Simulated effect of a reduction in forest surfaces on the number of trips and the consequent reduction in benefits. (Source: Tempesta et al. 2002)

Table 8.9 Housing prices

Table 8.10 Housing attributes considered in the model. (Source: Tyrvainen 1996)

Table 8.11 Hedonic price model (dependent variable: price per square meter). (Source: Tyrvainen 1996)

Table 8.12 Willingness to pay per hectare

Table 8.13 Conservation costs

Box 8.2 Estimate of the Landscape-Recreational Value of Forest Landscape Using the Travel Costs Method (Tempesta et al. 2002)

This study aims to verify the effects of territorial characteristics and activities on recreational demand. In particular, the territorial context of the research refers to various forest areas in the Friuli Venezia Giulia region of Italy.

The work involved several steps, described briefly below.

• (a) Data collection

  The first phase of the work refers to the creation of a territorial database containing information on landscape and territorial use, with geo-morphological variables (altimetry, presence of quarries/landslides, …), vegetation variables (arboreal coverage, tree species and relevant surfaces, …), anthropical variables (land use, cultivated surfaces, population density, …) and naturalistic variables (presence of parks, reserves, …). With reference to landscape use, data has been collected on the presence of refuges, high altitude camps and other accommodation facilities for tourists, along with the presence of paths. The information collected was integrated by numerous phone interviews with a sample of 516 people to collect information on their town, and find out how much they spent to take trips to the areas in question, their recreational habits, the accommodation facilities used on trips, their job, family unit and level of education, …

• (b) Elaboration and analysis of the results

  The data collected was elaborated using regression models to estimate the recreational value of the forest areas. The model developed compares the number of trips with the percentage of overall forest surfaces in the area (Table 8.7). The first column of the table shows the regression estimate, which gives an idea of the importance of the various parameters in determining the frequency of the number of trips; the following columns contain some coefficients used to assess the significance of the parameters obtained in statistical terms.

The influence of the percentage of woodland and grassland shows how important these are to guarantee a pleasant landscape and result, along with other factors, in greater attractive power for the visitor who will be willing to travel great distances to reach districts with a higher distribution of woodland. In consideration of the functional form calculated using the regression model, consumer surplus is equal to 3.22 € per trip. To obtain an initial estimate of the woodland landscape value, the number of trips was simulated with a 1% reduction of the forest surfaces in the areas considered. The result is that the reduction would be equal to 49,060 trips and the recreational benefits would drop by 157,776 €. The landscape value of a hectare of woodland is therefore equal to 58.77 € (Table 8.8).

Box 8.3 Landscape Value Estimate Using Hedonic Models (Tyrvainen 1996)

The application aims at evaluating external effects of urban forests associated with housing. Particularly, through the hedonic pricing method the works examines the benefits derived from pleasant landscape, clean air, peace and quiet and screening, as well as recreational activities. The research was developed according to different phases:

• 1. Data collection

  Apartment sales data (1,006 apartments) were collected in Joensuu, a town of 48,000 inhabitants in North Carelia, Finland. The information on purchase price and apartment characteristics were collected from documents received from local tax authorities. Furthermore, environmental and locality data were measured with respect to each specific house.

• 2. Elaboration

  According to the hedonic pricing method, the data collected was elaborated in order to explain purchase prices (P). Particularly, the model used the general formula P = f (A_i, L_i, E_i), where A_i is a vector of the apartment characteristics such as size, age and type of construction, L_iis a vector of the locality attributes such as accessibility to town centre, schools and shops, E_i is a vector of the characteristics describing the environmental quality in the housing district including variables such as accessibility to watercourse, recreation areas and relative amount of green spaces. Table 8.10 represents the observed characteristics.

Linear and log-linear hedonic price functions were calculated with multiple regression analysis. Table 8.11 shows the results of the regression models.

• 3. Results

  Results indicate that urban forests are an appreciated environmental characteristic and that their benefits are reflected in the property prices. Proximity of watercourses and wooden recreation areas as well as increasing proportion of total forested area in the housing district had a positive influence on apartment price. Particularly, Fig. 8.1 shows an application of the estimated implicit prices in evaluation of changes in the environmental quality: the greater the distance to the recreation area and watercourses, the lower the apartment price per square meter.

  Fig. 8.1

  

  Effects of changes in distance to recreation area and watercourse on apartment price per square meter. (Source: Tyrvainen 1996)

Box 8.4 Assessment of Expenses for the Conservation of Natural Landscape (Marangon and Tempesta 2008)

The results of various studies done in the Italian regions of Veneto and Friuli Venezia Giulia (Italy) in the 1990s to estimate the expenses born for the conservation of natural landscape are shown below. The innovative elements were: (a) maintenance of farm service roads; (b) maintenance of massive walls, dry walls, roadsides and terracing; (c) maintenance of historical artefacts (capitals, drinking troughs …); (d) maintenance of ditches and waterworks; (e) cleaning third party waste; (f) mowing plots of land for aesthetic reasons or safety; (g) cutting back shrubbery on pastures not used for productive purposes; (h) maintenance of non-productive woodland; (i) removal of fallen rocks from meadows; (j) maintenance of fences; (k) maintenance of hedges and trees. The interventions concern both the landscape in the strictest sense, and some functional actions for the use of the territory by visitors, and are therefore relevant for the utilization of the landscape goods.

The results of the specific analysis in the Colli Euganei area (in the province of Padua) are shown in Table 8.14 and Fig. 8.2.

Fig. 8.2

Distribution of the costs borne by farms for landscape conservation. (Source: Tempesta 1994, reworking)

Table 8.14 Average values of the costs borne by farms for territorial maintenance. (Source: Tempesta 1994, reworking)

In more general terms, Table 8.15 shows the detailed results of three specific studies carried out to establish the costs borne by farmers for the conservation of landscape in three different territorial contexts: a mountain community, hill country and lowlands. As we can see the costs decrease with the highest in the mountain community (179 €/ha), dropping for the hill country (132 €/ha), and lowlands (48 €/ha). In the mountain community the maintenance costs of the territory and landscape amount to over 16% of the farm’s marketable production. Furthermore, the composition of the costs differs on the basis of the zone: in the mountain community the costs for mowing meadows for aesthetic purposes, the maintenance of woodland and non-productive meadows are particularly high; in hill country and the lowlands there are more interventions for the conservation of the waterworks, hedges and of the inter-ponderal roads.

Table 8.15 Costs borne by farmers for the conservation of rural landscape elements. (Source: Marangon and Tempesta 2008)

Table 8.16 Tourism flows

Table 8.17 Value added

Table 8.18 Employment

Table 8.19 Amount of subsidies obtained

Box 8.5 Analysis of Tourist Movements in the Piemonte Region

Some data on tourist movements in the Piemonte Region of Italy is presented. On the basis of the data, we can examine the distribution of the presences in the various provinces and the accommodation structures used (hotels and other).

The elaborations of the data (Fig. 8.3) show that in general the movements on the regional territory have a positive trend, with a growth rate diversified between hotel presences and presences in other accommodation facilities. The latter, with accommodation in campsites, farm tourism enterprises and similar, is associated in particular with forms of “slow” tourism and territorial use.

Fig. 8.3

Tourist presences in the Piemonte Region in hotels and other accommodation facilities (2000–2007). (Source: Piemonte in cifre 2007, reworking)

It may be interesting to examine the distribution of tourist presences in the various geographical areas of the region (Fig. 8.4). The elaboration of data from the Regional Tourism Observatory shows that the hill country, combining the beauty of landscapes with the food-and-wine offer, represents the destination with the highest rate of growth in the regional territory. This is also evident in the following values from 2007, calculated in relation to 2006: +7.2% arrivals (529,953) and +4.6% presences (1,221,741).

Fig. 8.4

Tourist presences in the Piemonte regional territory in various geographical areas (2000–2007). (Source: Regional Tourism Observatory and Piemonte in cifre 2007, reworking)

Furthermore, the data on the tourist sector can be used to create thematic maps, to show the geographical distribution of the phenomena. The example in Fig. 8.5 indicates the data on the tourist sector in the Piemonte Region.

Fig. 8.5

Examples of thematic maps representing the data on tourist flows in the Piemonte Region concerning the national tourist presences (a), the international tourist presences (b), the number of bedspaces in hotels (c) and the number of bedspaces in other accomodation structures (d). (Source: Piemonte in cifre 2007)