Deforestation processes in Greece: A spatial analysis by using an ordinal regression model

Forest Policy and Economics 12 (2010) 457–472

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Forest Policy and Economics

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Deforestation processes in Greece: A spatial analysis by using an ordinalregression model

Dionysios Minetos 1, Serafeim Polyzos ⁎University of Thessaly, Department of Planning and Regional Development, Pedion Areos, Volos, 38334, Greece

⁎ Corresponding author. Tel.: +30 24210 74446.E-mail addresses: [email protected] (D. Minetos)

1 Tel.: +30 24210 74276.

1389-9341/$ – see front matter © 2010 Elsevier B.V. Aldoi:10.1016/j.forpol.2010.02.011

a b s t r a c t
a r t i c l e i n f o
Article history:Received 24 December 2008Received in revised form 19 January 2010Accepted 16 February 2010

Keywords:DeforestationLand use changeOrdinal regressionGreece

Forest land use changes in Greece have been the outcome of combining forces with mostly economic andinstitutional origin. Interactions between the major land uses have diachronically resulted in spatial patternsof great economic and environmental interest. This paper aims at describing forest land use changes duringthe last decades in Greece as well as analyzing the major regional and economic development implications.Particular attention is given to the analysis of possible driving forces with economic and social origin. Theestimations are carried out through the use of a statistical model that employs ordinal regression analysis.Ordinal regression is a variation of ordinary regression which is used when the dependent variable iscategorical and the explanatory variables are continuous, or categorical. An advantage of this type ofregression is that requires fewer assumptions as regards the relationship between the explanatory variablesand the dependent variable. Assessing sustainability of development decisions on a regional scale throughthe evaluation of likely impacts on forest resources can provide great support in formulating better regionalpolicies that incorporate the environmental protection objectives of the society.

, [email protected] (S. Polyzos).

l rights reserved.

© 2010 Elsevier B.V. All rights reserved.

1. Introduction

Access to sufficient empirical evidence on the causes of land usechanges represents a critical step towards improving understandingof land system functioning and for addressing the perplexinginteractions between people and their surroundings. Regardless ofits purpose, a reliable and consistent pool of evidence on theevolution and state of regional land use systems within the broadercontext of a country can contribute considerably to the improve-ment of strategic policy design and implementation (Briassoulis,2000; Agarwal et al., 2002). Nowadays, some of the most profoundland alteration phenomena are forest fragmentation and forest landuse conversion and modification (Geist and Lambin, 2001; Lambinet al., 2001). The processes that drive those types of changes arecomplex due to numerous dynamic interactions between naturalecosystems and human demand for utilising land for a diverse rangeof purposes.

Amongst others, forest land use changes occur formultiple reasonsand from interacting processes and mechanisms. Human-drivenchanges at an array of scales are affecting forest ecosystemsaccelerating processes such as global warming that affect humanwell being. Research has demonstrated that in the long term, there isnot a single factor or set of factors that can adequately explain theemerging patterns of land uses and the associated changes (Chomitz

and Gray, 1996; Angelsen and Kaimowitz, 1999; Geist and Lambin,2001; Lambin et al., 2001; Aspinall, 2004). Deforestation processeshave different characteristics across space and time (Verburg et al.,2006). A particular combination of factors that may explaindeforestation patterns somewhere might not be applicable forjustifying changes in any other location or time period (Mahapatraand Kant, 2005). In addition, forest fragmentation, conversion andmodification have diverse economic, social and environmentalimplications (Elands and Wiersum, 2001; Walker, 2001; Platt, 2004;Verburg et al., 2006) such as disruption in continuity of naturallandscape, forest and open-land squeeze between agricultural andurban land uses, deterioration of vital habitats that sustain valuablebiodiversity as well as broader issues such as air pollution. Therefore,there is a need for conducting empirical investigations in order toanalyse and understand the geographical and historical context ofland use changes.

This study primarily aims at: (a) revealing the major underlyingcauses of forest land use changes in Greece for the period from 1990 to2000 by using as a spatial scale of analysis the prefecturaladministrative level (NUTS III) (b) identifying the agents and natureof changes (c) examining whether some recent theoretical schematasuch as forest transition theory, can be used to effectively addresschanges in forest land at the regional scale and (d) assessing some ofthe implications of these changes. The above aims are pursuedthrough the estimation of the kind and magnitude of relationsbetween changes in forest land use and awide range of socioeconomicfactors. The methodology adopted is ordinal regression analysis. Thistype of regression is useful for processes that show some hierarchy or

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458 D. Minetos, S. Polyzos / Forest Policy and Economics 12 (2010) 457–472

grouping as well as when the data of land use change are not fullyreliable.

The paper is organised as follows. In Section 2 we provide aframework for the empirical analysis by dealing with forest land usechange driving factors and relevant theoretical and empirical researchthat refers to the process of deforestation. In Section 3 we present thestudy area, the ordinal regression model and the independentvariables used in the analysis. The overall performance of the modelis commented upon. Next, in Section 4, the results are presented andinterpreted. We conclude in Section 5 commending on the widerimplications of the findings.

2. Building a conceptual framework for deforestation

Recent attempts to theorise forest land use changes have yieldedsome noticeable contributions on the field of deforestation. One suchcontribution referred to as forest transition theory (Mather, 1992;Grainger, 1995; Mather and Needle, 1998) suggests that an overviewof forest land use changes in the long run, provides firm evidence thatwhile in the beginning forest land areas retreat at a high speed, atsome time the depletion starts slowing down. There is even a criticalpoint over which the process of depletion reverses and forest landrecovers by expanding into new areas. Prosperity level seems to havea key role in the whole process. The change in the monotony of therelationship between rate of deforestation and level of prosperitysuggested by the theory, shares some commons with Kuznets’Environmental Curve (Koop and Tole, 1999; Ehrhardt-Martinez et al.,2002) that links national and /or regional environmental quality withthe state of economic development. Mathematically speaking, therelationships is a curve of the form f (x)=αx2+βx+γ, α+βN1,where f (x) represents changes in environmental quality, χ refers tothe level of development in each spatial unit and α, β the coefficientsof the mathematical function.

Mather (1992) and Grainger (1995) argue that most of thedeveloped countries have been in a state of forest transition as forestland has been expanding for several decades. Explanations concerningforest transition processes are sought both in development theory andmodernization theory. These theoretical perspectives focus on theimportance of economic and social changes that spring from theadjustment of the economic, social and political structures totechnological advances. Technological innovations allow for increasedproductivity in the primary sector, limiting the needs for expansion onnew land and therefore the pressure on forest land retreats. A sizablebody of empirical research on forest transition theory has generatedconsiderable evidence in favour of some of the theory's reasoning(Rudel, 1998; Koop and Tole, 1999; Mather et al., 1999; Mather andNeedle, 2000; Ehrhardt-Martinez et al., 2002; Rudel et al., 2005;Rudel, 2007). Empirical evidence systematically suggests a negativerelationship between the rate of deforestation and the rate ofurbanization or the level of new technology adoption in the primarysector (Perz, 2007).

However, it should be pointed out that there are importantvariations concerning the assumptions, definitions and hypotheses ofthese empirical analyses, which need to be carefully taken intoaccount in the course of literature review, synthesis and evaluation.For instance, the definition of forest land varies amongst empiricalanalyses creating a fuzzy framework for investigating deforestation. Inaddition, pressure on forest land might result in varying outcomessuch as deforestation, forest degradation, fragmentation and changesin the structure of forests. These types of forest changes might not befeasible as well as sensible to compare to one another. There are alsoimportant gaps regarding the temporal dimension as well as the paceof advancement of forest transition procedure (Perz and Skole, 2003).Time needed for approaching low deforestation rates may differacross space, forest ecosystem type and social and political contextsFor example, it is uncertain whether there is enough available forest

land in small and medium-sized, developing insular regions toapproach low deforestation or even reforestation stages. In addition,the underlying causes related to either increasing or decreasing ratesof deforestation may not be stable in a spatiotemporal context.

In a recent attempt of improving the context of the theory,Angelsen (2007) suggests that the stages of forest transition theory(low deforestation, intense deforestation, containment of deforesta-tion, afforestation) can be better understood by focusing on thefundamental characteristics of the long-term relationship betweenagricultural land rent and forest land rent. Algebraically, this meansthat forest land use changes [DU]fr are a function of agricultural landrent [LR]agr and forest land rent [LR]frs of the type DUfst = f LRagr

LRfrs

� �. In

this case, the most important part of the analysis is to identify the wayand magnitude of influence of applied policies on agricultural andforest land rent. The aforementioned analysis by Angelsen focuses ontropical regions, where one of themost important proximate causes ofdeforestation is believed to be agricultural activity. However, in caseswhere urban land uses control the process of deforestation, therelationship between forest and urban land uses in terms of land rentmight constantly fuel urban expansion. There are large regions such asthe Mediterranean where the productivity of forests is low comparedto the productivity of other forests in Europe (Koutroumanidis et al.,2009). Therefore, it is almost impossible to expect Mediterraneanforests to antagonize successfully any other economic activity in termof financial returns as long as several components of forest totaleconomic value such as indirect use value (e.g. protection of humandevelopment), option use value, bequest value and existence valueare not properly considered.

An additional consideration regarding Agelesen's land rentapproach rests on the property regime of forest resources. In severalparts of theworld, the great share of forest land belongs to the state. Incases where the state cannot assure its one rights on land, there ismore scope for forest land encroachment and exploitation throughlogging and cultivation and even urbanisation. This is frequently thecase in Greece where there does not exist a forest state cadastre. Insuch cases, forest land use change processes maybe better understoodon the basis of Hardin's theory of the tragedy of the common (Hardin,1968). Individuals when act independently might have nomotives forconsidering the need for a sustainable use of shared natural resources(Herschel, 1997).

Approaches on a higher spatial scale of analysis regardingunsustainable forest resource exploitation are not scarce. Severalresearchers (Roberts and Greimes, 2002; Shandra et al., 2003),drawing from the theory of dependence by Baran, Frank and Amin aswell as from world systems theory by Wallerstein, claim that thedeveloped regions have established a particular system of economicexchange that imposes certain land use patterns to the less developedregions. Power, wealth and prosperity are related to the depletion offorest resources in the developing regions and to the sustainable useand possible expansion in prosperous regions. Within the context of acountry, it might be interesting to examine the influence of leading,usually populated regions on land use patterns of their adjacent lessdeveloped rural regions. In turns, this raises the issue of howaccessibility in space as well as observed spatial patterns of wealthlink to deforestation.

At the human decision level of analysis, the importance and theadaptive nature of strategies employed by agents in order to maintaintheir prosperity level have been firmly established in the context ofmulti-phasic response theory originally proposed by Davis (1963). It isnow believed that applied more broadly, multi-phasic responsetheory can help us understand how agents' decisions impact landuse changes (Lambin et al., 2006). A similar body of approachesattempts to apply concepts from game theory in order to captureagents’ behaviour in the course of forest land use change (Fredj et al.,2004). According to some of these perspectives, the major decisiveforce of the way forest resources are utilized is state policies. State

459D. Minetos, S. Polyzos / Forest Policy and Economics 12 (2010) 457–472

policies are far from static paying particular importance to economicgrowth especially during periods of economic difficulties whereenvironmental concerns are ranked low in social agenda. Bearing inmind the structure of the Greek economy, policies with a strongspatial dimension such as tourism accommodation infrastructurepolicy, land use and housing policy as well as agricultural policy arecapable of impacting regional forest resources. Yet, in the context ofgame theory it is proposed that it is possible to arrive to sufficient andsustainable solutions to the issue of deforestation through coopera-tion and coordination of the involved parties and individuals (Fredjet al., 2004; Stern, 2006).

On the other hand, there is a quite different view concerning thecurrent social behaviour and state intervention towards forestresources. It is widely believed that contemporary environmentally-friendly policies such as afforestation policies are not merely anopportunistic reaction to the undisputed acute depletion of forestresources and its associated impacts. Instead, they may revile a muchdeeper transformation of social attitudes and ethics towards theenvironment. Mather et al. (2006) argue that at least in the case ofdeveloped countries, a great part of society is driven by the principlesof post-productivism philosophy. Profit maximisation is not the only aswell as the central axis of individual behaviour formation. Therefore,land use changes in the countryside could possibly be better under-stood in light of society's priorities in the context set by post-productivism. In this respect, Maslow's theory of the hierarchy of needssustains considerable potential of explaining certain emerging landuse conversions as well as land use qualitative modifications inexurban land use systems.

Recently, the concept of post-productivism has been enriched witha spatial dimension grounded on the observation that some regionshave developed stronger post-productivism structures than others(Marsden, 1998; Mather et al., 2006). Post-productivism is thought ofas a spatial phenomenon too, that is strongly connected to the ruralpatterns of land uses. Amongst others, Marsden (1998), Groot et al.(2007) and van der Ploeg et al. (2000) underline the fact that thereexist considerable spatial differences within developed countries atthe regional level in relation to the intensity that one can observeevidence of post-productivism. Nevertheless, Mather argues that inspite of the observed spatial differences in the strength of post-productivism characteristics amongst developed countries andregions, the phenomenon is present and lies on deep social andcultural transformations in progress. Such transformations arecapable of inducing constructive institutional interference which isboth a cause and a consequence of new policy planning andapplication.

Summing up the discussion of the above perspectives, it can beargued that the common ground between the aforementionedtheoretical schemata lies on the ascertainment that exurban landuse conversions and modifications are advancing at a high rate(McCarthy, 2005, 2008). To some theorists, those changes arecircumstantial or adventitious reactions of invested capital on ruralareas in order to protect and insure its reproduction (Evans et al.,2002). Therefore, the current trajectories of rural land use changescould rapidly shift to new directions due to changes in the globaleconomic environment. On the other hand, a growing body ofarguments suggest that emerging land use patterns rely on deeperand stable changes in fundamental characteristics of society in termsof people's attitude towards natural environment (Inglehart, 1990;Buttel, 2000). However, what is truly happen in the rural land usesystems is still a matter of concern and an issue for study andevaluation. In sections that follow, we attempt to produce empiricalevidences on some of the theoretical arguments commented above inthe case of recent forest land use changes in Greece.

Following the literature review on deforestation, it can be said thata variety of economic and social forces might work competitively oradditively towards the configuration of land use patterns across the

regions of a country (Verburg et al., 2002, 2004, 2006). Underneath,we make an attempt to connect all important aspects of deforestationinquiry in a coherent conceptual framework. The process of forestconversion and modification on the regional level is presentedschematically in Fig. 1.

The underlying causes of forest land use change vary from localityto locality as well as amongst countries (Wood and Skole, 1998;Lambin et al., 2001; GLP, 2005). These economic, socio-cultural andpolitical forces are capable of impacting negatively or positively theextent and distribution of forest land. Their influence on forestsusually results in quantitative as well as qualitative changes. Bothconversions and structural habitat modifications are of greatimportance to policy makers. Reality is even more complex becausethere are usually linkages and interactions between positive andnegative factors of change (Mahapatra and Kant, 2005). The likelyaggregate outcome of the combining action of negative and positivefactors is difficult to understand and predict. However, it may bemorefeasible to identify some dynamic processes through which socioeco-nomic and political factors operate resulting in distinct patterns ofland uses. In this respect we propose urban sprawl, location decisionsof critical economic activities such as tourism, agricultural expansionand agricultural abandonment as the main trajectories of forest landuse change. In the sections that follow, we make an attempt to movefrom conceptual framework to empirical inquiry by identifying anempirical model along with the likely variables that hold someimportant potential of explaining recent deforestation processes inGreece. Some of the aforementioned theoretical schemata are testedand commented upon.

3. Empirical analysis

3.1. Study area

Greece consists of 13 administrative regions, which are furthersubdivided into 51 prefectures (Fig. 2). The country covers a total areaof some 131,957,4 km2. The mainland part of the country marks theend of the Balkan Peninsula whereas the insular part (about 3,000both habited and inhabited islands situated in the Aegean Sea as wellas the Ionian Sea) borders with Asia and Africa continents. Coastlinestretches for approximately 15,000 km. The country's population isapproximately 11 million people with 72.8% staying in urban areasand the remaining 27.2% being rural population. In mountainous areaslives 9.2% of the population, whereas in semi-mountainous and urbanareas the figures are 21.8% and 69.0% respectively. Agriculture andpastoral uses cover 49.5% of country's surface, forests, shrub and bareland cover 47.2%, inland water 1.3% and urban and other artificialsurfaces cover 2.0% (NSSG, 2004b).

Geomorphologically speaking, most of the mainland territoryconsists of mountains. Just a few major agricultural plains exist thelargest of which is placed in central Greece in the administrativeboundaries of the Thessaly region. The country has a Mediterranean-type of climate with high temperatures and very little rainfalls in thesummer. However, the area has relatively mild winters. The mostcommon plant communities include forests of broadleaf evergreentrees, oak woodlands, sclerophyllic bushes of maquis and garrigue. Onmost of the insular areas maquis communities may be the climax.However, in the mainland parts of the country this type of vegetationhas usually been the outcomeof forest dislocation and overexploitation.

3.2. Variables selection

A total of 10 variables describing economic, social and physicalcharacteristics were employed for the empirical analysis. As areferencing spatial unit for statistical analysis it was used theprefectural administrative level which corresponds to NUTS III levelof Eurostat. The selection of independent variables relied on both the

Fig. 1. Forest land use conversion and modification process.


relative availability of data and the literature review on deforestationfactors presented earlier. Certain country-specific processes regardingmostly building activity (e.g. informal housing) were taken intoconsideration.

Fig. 2. Spatial distribution of de

In the beginning, we started with a “logical” model of thephenomenon and then we subtracted some independent variablesbased on significance level as well as on logical hypotheses on the waythose independent variables affect the dependent variable, keeping

forestation rates in Greece.


always in mind not to perform a lot of mechanical applicationsbecause this could affect the regression results. Unless we wanted totest some theory or a critical hypothesis, we dropped the non-significant variables with a p-value of more than 0.25 making themodel more parsimonious. One such variable used in the originalmodel but not included later on was the total area burned by forestfires in each prefecture. It was found to be highly correlated with thevariable representing informal housing activity (X10). We decided todrop the first variable from the model because forest fire is the meansof changing the use of land whereas building a house is the realpurpose of changing it. Some additional variables used in the originalmodel were: (a) the geographical zone of each prefecture, a variablethat mostly refers to the climatic zone of the prefectures, (b) totalpopulation potential, a variable incorporating both indirect popula-tion potential and direct population potential and (c) theMean GDP inAgriculture. With the chosen link function, the additional variablesmentioned above did not contribute to the model. Table 1 provides anoverview of the explanatory variables that were finally employed inthe analysis of deforestation in Greece for the period 1990–2000.

3.2.1. Deforestation: the dependent variableData for estimating changes in total area covered by forests

between 1990 and 2000 at the prefectural level were derived fromtwo surveys of NSSG (NSSG, 1995, 2004b). The observed changes areexpressed by the ratio of the amount of forest land for the year 1990 tothe amount of forest land for the year of 2000 in each prefecture. Theoriginal data were transformed in order to be compatible because theNSSG used different classification categories for recording forest landin the aforementioned surveys.

Clearly, this raises some questions regarding data quality. Twosources of potential errors may exist. Firstly, the recording processesthemselves employed by NSSG might have failed to identify the exact

Table 1Description of the variables used in the ordinal regression model.

Variable Symbol Description

Dependent variableRate of deforestation Y The ratio of the area covered by forests at the pref

level in 1990 to the corresponding area in 2000

Independent variablesUrban sprawl X1 The ratio of the number of buildings constructed o

formal city and town borders in each prefecture tonumber of building in that prefecture, up to the ye

Geographicalcharacter

X2 Binary factor with a value of 1 in case of an insulaor a value of 2 in case of a mainland prefecture

Change in ruralpopulation

X3 The ratio of rural population in each prefecture foryear 2001 to the rural population for the year 199

Indirect populationpotential

X4 Indicator of accessibility of each prefecture in relato the rest of the prefectures.

Urban plan expansion X5 The total area per 100 residents incorporated withboundaries of towns and cities in each prefecturethe period 1995–2003.

New legal housing activity X6 The area in square meters per resident added to thbuilding area stock for the period 1997–2000

Prosperity level X7 The contribution of each prefecture to the GNP ofand to GNP per capita in € as well as in PurchasingPower Standards (PPS)

Change in GDP inagriculture

X8 The change in the percentage with which the GDPin each prefecture contributes to the formation ofagriculture for the whole country for the period 1

Change in hotel beds X9 The ratio of the number of hotel beds for the yearthe number of hotel beds for the year of 2001 in e

Informal housing activity X10 Total number of legalised housing units per 1000per prefecture for the period 1997–2006

a 1 Stremma=1000 m2.b Complex unit of measurement not holding any importance for the purpose of the pres

area in each prefecture covered by forests both during the 1990 and2000 surveys. Almost all recording methods have merits and flaws.Such errors are likely to change the actual order of prefectures acrossthe range of values of the continuous variable (rate of deforestation)creating amisleading pattern. Secondly, the difference in classificationcategories between surveys of 1990 and 2000 is also likely to haveaffected the actual value of deforestation rate because one comparesin the deforestation ratio similarly but not identically produced datasets. Bearing in mind the above considerations, it seems highly riskyusing the continuous values of the variable in a statistical analysis.

Therefore, because the data were considered to be of questionablequality we decided to treat the dependent variable as a categoricaland not as a continuous one. The advantage of constructing broadcategories of deforestation is that even relatively large errors duringthe recording procedure do not affect the results decisively. Adjacentprefectures across the range of values of the continuous variable areclassified in the same category assigned a code value. In this way,neither the exact ordering of prefectures nor the actual value ofdeforestation can decisively affect the results of multivariatestatistical analysis. The drawback, however, is that the new categoricalvariable is less informative than the original continuous one. Thecategories of the dependent variable in which the prefectures whereclassified according to their rate of forest land use change were thefollowing:

• Category 1: prefectures with low deforestation rate (+0.86)• Category 2: prefectures withmedium deforestation rate (0.81–0.85)• Category 3: prefectures with high deforestation rate (0.71–0.80)• Category 4: prefectures with very high deforestation rate (b0.70)

Fig. 2 is a representation of the spatial distribution of deforestationrate during the study period across the spatial units of the country. It isobvious that several insular locations in the Aegean as well as the

Unit/scale ofmeasurement

Data source/years or range associated to eachvariable

ectural Dimensionless (NSSG, 1995, 2004b)Ratio converted toordinal

1990, 2000

utside thethe totalar 2000.

Dimensionless (NSSG, 1994, 2004a)Ratio 1990–2000

r prefecture Island or Mainland NSSG (2004b)Nominal Not applicable

the1.

Dimensionless (NSSG, 1999, 2004b)Ratio 1991, 2001

tion Citizens/min−1.5 (Ministry of Economy & Finance, 1993; NSSG,2004b; Polyzos and Arabatzis, 2006)

Interval 2001in thefor

Stremmasa/100residents

YPEHODE (2006)

Ratio 1995–2003e existing m2/resident NSSG (2006)

Ratio 1997–2000Greece Complex unit of

measurementb(Petrakos and Polyzos, 2005)

Interval 2000in agricultureGDP in990–2000

Dimensionless AllMedia (2006)Ratio 1990,2000

1991 toach prefecture

Dimensionless AllMedia (2006)Ratio 1991, 2001

residents Legalized buildings /1000 residents

NSSG (2006)

Ratio 1997–2006

ent analysis.


Ionian archipelagos present high deforestation levels. As regards themainland prefectures of the country, there are several non-costalprefectures (e.g. Kozani, Kilkis, Florina, Grevena) that have high orvery high deforestation rates. On the other hand, most of the highlyurbanised prefectures (e.g. Attiki, Achaia, Magnesia) show low ormedium deforestation rates.

3.2.1.1. Urban sprawl. The dominant development pattern of the oldcities and towns in Greece used to be the compact one but over thepast 50 years the density of residents per unit of area has declinedconsiderably. Historically speaking, the configuration of housingareas in Greece was mostly driven by the unorganised individualattempts of people to shelter themselves by own means rather thana coherent long-term strategy by the state for providing housingspace to the population (Leontidou, 1989; Leontidou et al., 2001;Lavvas, 2002; Mellisas, 2007). In contemporary times, the produc-tion of urban space has still been relying on individuals' actionsdespite of the existence of a great variety of planning tools andincentives at least at the institutional level. As a result, per capitaland consumption increases steadily and urban land uses arerandomly sprawling into the countryside without following anintended and well-designed pattern.

Moving outside of the borders of the formal urban plans poses greatpressures on the natural environment resulting in habitat shrinking aswell as forest and agricultural land uses conversion and modification.Numerous studies on land use change consider urban sprawl as amajordriver of deforestation (Walker, 2001). However, it is not clear whetherthe outwards sprawl of urban forms takes place mainly on agriculturalland or on forest land in Greece. Therefore, we set to examine whetherurban sprawl impacted forest resources during the study period orinstead, only the agricultural land was affected.

3.2.1.2. Geographical character. According to the coding scheme of thisvariable, the 51 administrative units of the country have beenallocated into 2 groups. The first group includes the insularprefectures whereas the second group incorporates the remainingmainland prefectures. By using this variable we investigate whetherthe geographical character of a prefecture has an effect on defores-tation rate. During the last few decades, insular areas in Greece havebeen exposed to intense developmental pressures which primarilyderive from tourism related activities. At the same time, both the ratioof forest to non-forest land and the actual extent of forest areas onislands, are of the lowest compared to the rest of the prefectures of thecountry. Due to limited geographical space, insular forest resourcesare much more vulnerable to developmental pressures. Therefore, weexpect that deforestation possesses will be more acute in the case ofinsular spatial units.

3.2.1.3. Change in rural population. The variable representing ruralpopulation dynamics is entered in the model in order to assess theinfluence on deforestation of contemporary rural demographic pro-cesses. Bearing in mind that the past phenomenon of rural-urbanmigration has long been ceased in Greece, we want to investigate theinfluence of the opposite process of rural rebound. As reported in a partof the relevant literature (Dahms and McComb, 1999; Brown et al.,2005), population-driven land use changes can be the outcome ofpopulation migration towards the rural areas (demographic ruralrebound). In the case of Greece, when the country got into the developedstage, post war massive rural-urban migration movements phased outand a reverse process of people seeking dwelling in exurban rurallocations started. Themassive growthof tourismrelatedactivities aswellas the provision of new public infrastructures (transportation infra-structures, health and education facilities etc), resulted in the improve-ment of standards of living and the creation of additional employmentopportunities. Several insular and coastal regions in Greece, havemanaged to increase their populations during the study period.

3.2.1.4. Indirect population potential. Indirect population potential is ameasure of the level of accessibility of each prefecture to the rest ofthe prefectures of the country. Geographical space, transportationinfrastructures and time, play an important role in shaping people'spreferences as to where to live and work. Therefore, space-timeaccessibility is intimately connected to land use patterns. We use thisindicator because, very often, changes in the use of land in a locationare generated by people who live and work away from that location.Residents of large urban concentrations may sometime choose tobuild houses or undertake other forms of land use transformations inadjacent prefectures. As regards housing activity, this tendency isknown as secondary and/or vacation housing and happens across thecountry in many instances (e.g. dwellers of Thessaloniki buildvacation houses in Pieria or Chalkidiki whereas dwellers of Athensprefer the coastal part of Korintia and the island of Evia to place theirsecondary houses). The indicator of indirect population potential hasbeen estimated by using the following equation (Clark et al., 1969;Keeble et al., 1982):

IPPi = ∑50

j=1

PjDaij

ð1Þ

where:

IPPi The indirect population potential of region i.Pj The population of region j, where j=1,…,50 (fifty is the

number of Greek prefectures minus one).Dij A measure of the time-distance between regions i and j.α The superscript α is a measure of distance “friction”.

For the purpose of the present study, the α component is set to 1.5,a value suitable for movements of general purpose (Wang, 2000; LuoandWang, 2003). Data for this variable come from the NSSG (2004b),Polyzos and Arabatzis (2006) and the Ministry of Economy andFinance (Ministry of Economy & Finance, 1993).

3.2.1.5. Urban plans expansion per 100 residents between 1995 and2003. This variable represents the expansion of urban plans in eachprefecture for the period from 1995 to 2003. During that period, thearea that was incorporated within the boundaries of towns and citieswas partly for meeting the demand of that time for urbandevelopable land and partly for legalising build-up areas of informalhousing units that were constructed during the early 90s. Thisvariable was estimated by using unpublished data from the Ministryfor the Environment, Physical Planning and Public Works (YPE-HODE, 2006). The procedure for expanding urban plans is complexand also takes several years to finish. Amongst others, it involves thedecision of initiating the process of expansion, the production ofseveral studies and plans, consultation with the relevant authoritiesand the public and the final decision on the expansion. So, weconsidered the expansions that the process started after 1990 up to1997 under the provisions of Law 1337/1983. The procedures forthese expansions were finished between 1995 and 2003. After 1997,the expansions of urban plans were conducted under the provisionsof Law 2508/1997. With the new legal framework in place, the firstapplications for new expansions were submitted after 1999. Theintention behind the use of this particular explanatory variable wasto evaluate whether the supply of new buildable land throughformal planning procedures had a significant effect on loweringantagonism between forest and urban land uses or it was insufficientin halting deforestation due to urban expansion.

3.2.1.6. Legal housing activity per resident for the period 1997–2000.The indicator of legal housing activity in each prefecture depicts thearea in square meters per resident added to the existing buildingstock for the period 1997–2000. This areawas added through the legal


procedureof issuinga building licenceprior to undertaking constructionworks. The approved construction works were materialised eitherwithin the formal townand city urbanplans oroutside theseplans in thecountryside provided that all legal requirements (e.g. sufficient size ofland-plots etc) had been met. As long as forests have a strong degree ofprotection under the Hellenic Constitutional Principal (articles 24 and117, 1975/86/00) “forest use changeonly for thepublic interest andonlyin cases in which the public interest cannot be accommodated for byalternative means that do not include forest land use change”, the legalhousing activity cannot directly affect forest resources in anegativeway.Anyobservednegative association should bedue to indirect detrimentaleffects of legal housing such as increased possibility for accidental forestfires etc. However, this would mean, that despite the fact of being legalon institutional terms, this particular housing activity has not yet beensustainable, threatening forest resources through indirect detrimentaleffects. The data for this variable come from the NSSG (2006).

3.2.1.7. Prosperity level. The prosperity indicator has been estimatedby using the official data at the prefectural level by Eurostatconcerning the contribution of each prefecture to the GNP of Greeceand to GNP per capita in € as well as in Purchasing Power Standards(PPS). Due to the fact that the per capita GNP cannot give a safeestimation of the prosperity in the NUT II and III levels, they havealso been incorporated into the variable additional financial anddevelopment indicators concerning the levels of consumption aswell as public infrastructure in each prefecture. The data concerningthis variable come from a previous study by Petrakos and Polyzos(2005). As it was mentioned earlier, in the context of foresttransition theory several researchers argue that in the long run therelationship between deforestation and prosperity level tents to benegative meaning that as the prosperity level raises the rate ofdeforestation drops. By introducing this variable in the empiricalmodel we attempt to investigate whether the level of humanprosperity in each prefecture is connected to increased forest landuse changes or the observed changes do not depend on the level ofeconomic development of each prefecture.

3.2.1.8. Changes in GDP in agriculture. This variable represents thechange in the Gross Domestic Product (GDP) in agriculture to theGross National Product (GNP) in agriculture for the period from1990 to 2000 at the prefectural level. The relevant changes wereestimated by using the following formula:

AgriGDPi−1990.AgriGNP1990

AgriGDPi−2000�AgriGNP2000

ð2Þ

where,

AgriGDPi_1990 The mean GDP in agriculture in the years 1989, 1990,1991 for prefecture i

AgriGNP1990 The mean GNP in agriculture in the years 1989, 1990,1991 for the country

AgriGDPi_2000 The mean GDP in agriculture in the years 1989, 1990,1991 for prefecture i

AgriGNPi_2000 The mean GNP in agriculture in the years 1999, 2000,2001 for the country

As the share of GDP in agriculture of a spatial unit to the GNP inagriculture increases, agricultural activities in that area would eitherexpand or become more profitable through the introduction ofinnovations, new technologies and cultivation practices. Bearing inmind that the relevant statistical data revile a reduction in agriculturalland in all 51 prefectures in Greece, we could hypothesize thatincreased profitability in agriculture would probably been the

outcome of structural changes in the primary sector and not due toexpansion of agricultural land into forestland. If this is the case, thenwe expect to observe a negative relationship between deforestationand change in GDP in agriculture meaning that any improvement inthe economic results of agricultural sector would had no adverseeffects on the extend of forest land.

3.2.1.9. Changes in hotel beds for the period 1994–2000. Severalenvironmental problems such as extensive deforestation are fre-quently associated with the development of tourism activities.Deforestation and forest degradation are in many instances the resultof increased, tourism-driven demand for land plots located in areaswith specific high-quality natural characteristics. Part of the relevantliterature on the environmental impacts of tourism (Campbell et al.,2000; Gossling, 2002) suggests that areas with natural characteristicsof outstanding quality are more likely to receive increased pressurefrom tourism related activities. The economic forces of tourismmarket through utility maximization procedures are likely to result inunsustainable development patterns where natural qualities aredegraded.

In the context of the present research, the likely linkages betweentourism activity and change in forest resources are investigated byincorporating into the statistical analysis an indicator of tourisminfrastructure as well as tourism demand. Assuming that the variable“changes in hotel bed” reflects both the magnitude of new tourisminfrastructure for the period under investigation as well as thedemand for vacations in Greece, we look into the impacts of tourismon forests on the prefectural scale of analysis. Changes in hotel bedsare expressed by the ratio of the number of hotel beds for the year1990 to the number of hotel beds for the year of 2000 in eachprefecture. Modern tourism infrastructure results in profoundchanges to the landscape especially on the coastal and insularlocations. We expect that large changes in tourism hotel infrastruc-ture might have a detrimental effect on forests.

3.2.1.10. Informal housing activity. Informal settlements in Greece forma complex issue. This particular strategy of constructing housing unitsis associated with a population of a wide variety of social andeconomic backgrounds. Therefore, informal housing units do not havethe same characteristics everywhere and the quantification of the realmagnitude of the phenomenon is an extremely difficult task(Maloutas, 2000). However, the individual cases share some commoncharacteristics and can be classified into certain general categories.One such category concerns houses created by low-income house-holds that they are scattered all over the country both close to urbancentres and in ex-urban areas. A second category is made up by luxuryvacation or secondary houses, placed close to the coastal zone or toremote rural locations. Finally, a third category encompasses theremaining cases such as illegal business buildings of the primarysector (e.g. farm buildings), the secondary sector (e.g. small familymanufacturing companies) as well as the tertiary sector (tourism-related buildings).

We introduce this particular variable into the analysis attemptingto investigate whether or not illegal housing phenomenon has anassociation with the observed forest land use changes. If such arelation is present, then it could be asserted that despite theexistence of certain pieces of legislation that prohibit the construc-tion of housing units on forest land, the production of an important –but still difficult to quantify – part of urban space is taking place onforest land undermining local communities' prospects of sustainabledevelopment.

For estimating informal housing activity, we have used data oninformal housing units that entered into the legalisation process duringthe period from1997 to 2006. This time period of data setwas chosen insuch a way as to refer to land use changes between 1990 and 2000.Wehave chosen to cover this period assuming that most of these housing

Table 2Test of parallel linesc.

Model −2 log likelihood Chi-square df Sig.

H0: null hypothesisd 79,540General 54,838a 24,702b 28 0.644

a The log-likelihood value cannot be further increased after maximum number ofstep-halving.

b The chi-square statistic is computed based on the log-likelihood value of the lastiteration of the general model. Validity of the test is uncertain.

c Link function: Cauchit.d The null hypothesis states that the location parameters (slope coefficients) are the

same across response categories.

Table 3Model fitting informationa.

Model −2 log likelihood Chi-square df Sig.

Intercept Only 140,222 – – –

Final 79,540 60,682 14 0.000

Pseudo R-square Goodness-of-fitCox and snell 0.696 Chi-square df Sig.Nagelkerke 0.743 Pearson 228,888 136 0.000McFadden 0.433 Deviance 79,540 136 1.000

a Link function: Cauchit.


units were built in the 90s – therefore causing land use changes in thatperiod – but they were legalised later on. In particular, there is usually atime-gap of several years between constructing an illegal house andgetting detected by the authorities. Taking into account the new Greekcity planning law of 1997 (Act 2508/1997), we considered the illegalhousing units recorded after 1997 up to 2006. Most of these had beenconstructed in the 90s and not earlier, changing the use of land at thatperiod. This is true if we bear inmind that, from 1983 to 1987 there wasmade a huge effort by the Greek state (Law 1337/1983) to record andlegalise most illegal housing units constructed after 1955 throughoutthe country (the so-called Urban Planning Reformation). Therefore, themajority of illegal buildings constructed up to the late 80s, had alreadybeen recorded by the authorities. Finally, we also assume that thelegalised housing units which are only a fraction of the total informalhousing activity in Greece, are proportional to the ones that still remainillegal. Data for this variable come from the relevant annual buildingactivity tables published by NSSG (NSSG, 2006).

3.3. Model description

The relationships between deforestation and its likely driving factorsare evaluatedbyusing anordinal regressionmodel. Thismethodologyhasnot beenusedasoften aspolychotomous logistic regressionor other typesof multivariate statistical models in land use studies. However, there aresome relevant applications in the international literature that have beenuseful in understanding certain aspects of land use change processes. Arelative application has been conducted by Fu et al. (2006) in order toevaluate changes in agricultural landscape pattern between 1980 and2000 in the Loess hilly region of Ansai County, China. A second applicationwasconductedbyRutherfordet al. (2007). Theyappliedbothmultinomialand ordinal regression models in the investigation of patterns ofsuccessional change after agricultural land abandonment in Switzerland.

Ordinal regression can take into consideration and introduce intothe calculations some of that extra information in the ordinal scale ofthe response variable (Norusis, 2004). Therefore, the methodologycan be used to analyse the degree of change of a particular land-use(low, medium or high change) when it is not possible to capture thisby a continuous variable or proxy. There are five different link functionsthat can be used in the construction of an ordinal regression modeldepending on the escalation of the response variable's cumulativeprobability (Norusis, 2004; SPSS, 2007).Weuse theCauchit link function(reverse Cauchy) because the escalation in cumulative probability of thedependent variable increases from0 fairly rapidly, then slowsdown andfinally accelerates when it approaches 1. The Cauchit link takes the formlink (γij)=tan (π(γij−0.5)). The ordinal regression model instead ofconsidering the probability of an individual event occurring, it estimatesthe probability of that event and all events that are orderedbefore it. Thegeneral model for ordinal regression is:

link γij

� �= αj− ∑

k

n=1βkXk with γij = Prob Y≤j jxið Þ = ∑

j

l=1πil ð3Þ

where,

link (γij) The abovementioned Cauchit link. The index j refers to the

subcategory within the land-use type of investigation (e.g.low deforestation,medium deforestation, high deforestation,very high deforestation).

Y The response variable, which takes integer values from 1 to j.γij The cumulative response probability up to and including

Y=j at subpopulation i.Xk The k predictor variables associated with the observed

changes in the dependent variable.αj The intercept of the regression equation or threshold for

each cumulative probability. The index j refers to thesubcategory within each land-use type.

βk The coefficients of the predictor variables or else thelocations of the model. The threshold αj and the regressioncoefficient βk are unknown parameters to be estimated bymeans of the maximum likelihood algorithm.

Πij The cell probability corresponding to Y= j at subpopulation i.

Having chosen the Cauchit link the general model is written asfollowing:

tan π γij−0:5� ��

= αj− ∑k

n=1βkXk ð4Þ

In the present study the event of deforestation has been defined asfollowing:

ω1 = tan π prob deforestation≤lowð Þ½ �−0:5½ � = α1− ∑k

n=1βkXk ð5Þ

ω2 = tan π prob deforestation≤lowð Þ½ �−0:5½ � = α2− ∑k

n=1βkXk ð6Þ

ω3 = tan π prob deforestation≤highð Þ½ �−0:5½ � = α3− ∑k

n=1βkXk ð7Þ

Because it is often the case that the magnitude of several spatialphenomena such as deforestation depends not only on the operation ofsome single factors but on the joint action of two or more factors at theappropriate levels,wehave also decided to add some interaction terms tothe model. In this way we investigate the likely joint effects of somevariables on deforestation over and above their separate effects. The firstinteraction term introduced into the model involves the variables ofindirect population potential and informal housing activity. In a spatiotem-poral context, informal housing activity is not always and everywhereforest damaging. “Soft” construction illegalities such as building morestores and space on the ground than the building licence permits, do notaffect negatively forest resources at list as much as “hard” constructionillegalities like building housing units directly on forest land. In addition,according to forest transition theory, during initial stages of developmentforest resources are affectedmore than in later stages of development. Byintroducing this interaction term in the analysiswewanted to investigatethe hypothesis that when accessibility improves multiplying opportuni-ties for development in a locality, the kind of development performed at


initial stages is environmentally unsustainable and thus damaging toforest resources as proposed by forest transition theory.

The second interaction term introduced into the model involves thevariables of urban sprawl and change in tourism accommodationinfrastructure. In Greece, the building industry is a significant driver ofboth economic growth and land use change. However, not all kinds ofsprawling urban formshave the sameharmful effects on forest habitats interms of land use change. Industrial buildings and blocks of apartmentsneed not be located close to natural resources such as forests. Contrarily,it is common that tourism accommodation infrastructures opt forsituating in environmentally advantageous location, usually close toMediterranean coastal forests. Therefore, we attempt to test thehypothesis that certain qualitative characteristics of sprawl are likelyto make it much more harmful to forest land uses. If we take intoaccount that the final model included 10 explanatory variables as wellas the above mentioned interaction terms, then the model can bedefined as following:

tan π prob score≤categry→jð Þ−0:5½ �½ � = αj

�ðβ1X1 + β2X2 + β3X3 + ::::

+ β10X10 + γ1X4*X10 + γ2X1*X9Þð8Þ

3.4. Model fitting information

Some summary statistics of the model are presented in AppendixA. In particular, it can be seen the coding scheme and the selected cut-points for the dependent as well as all categorical explanatoryvariables, the number of prefectures that fall into each individualcategory of the categorical variables and the marginal and cumulativepercentages. When using an ordinal regression model, there is a strictassumption that has to be made. This is the parallel lines assumption.That is to say, the regression coefficients are equal for allcorresponding outcome categories. Therefore, it is extremely impor-tant to carry out the test of parallel lines and if the assumption fails,the multinomial logistic regression model can be used as analternative model. The test of parallel lines compares the estimatedmodel with a single set of coefficients for all categories to a modelwith a separate set of coefficients for each category. Table 2 presentsthe test of parallelism namely the assumption that the regressioncoefficients are the same for all four categories of deforestation. As wecan see, the assumption of parallelism cannot be rejected because thelevel of statistical significance for the general model is 0.644.Therefore, we sustain the null hypothesis that the location parametersare the same across the response categories.

Table 3 presents the Pearson and Devience Goodness-of-Fitmeasures. The observed significance levels are 0.000 and 1.000respectively. These values are unusual, meaning that the model'salgorithm was disrupted during the calculation of the measures. Theprobability of disruption is high when there are many empty cells inthe crosstabulations of the model. Therefore, we should not rely onthese measures because the number of empty cells in the model'scrosstabulations was large due to the use of several continuousdependent variables (there was a warning in the output of the modelthat there were 153 or 75.0% cells with zero frequencies). As regardsthe overall-model test of the null hypothesis that the locationcoefficients for all of the predictor variables in the model are zero, ityields a significance level lower than 0.01. Therefore, we candetermine that the intercept-only model does not perform betterthan the model with the predictors. This is an important test becausethe change in the likelihood ratio has a chi-square distribution evenwhen there are cells with small observed and predicted counts(Norusis, 2004). Finally, the pseudo-R2 measures evaluate the successof the model in explaining the variations in the data which is anindication of the strength of associations between the response

variable and the independent variables. The pseudo R2 for McFadden(0.433), Cox and Snell (0.696), and Nagelkerke (0.743) can beconsidered satisfactory as the values of pseudo-R2 measures arealmost always much smaller than the R2 of a linear regression model(Norusis, 2004). Therefore, the interpretation of pseudo-R2 needs tobe careful.

Finally, the confusion matrix that follows (Table 4) provides asummary of the predictive ability of the empirical model. As we cansee, in the first category of low deforestation prefectures the correctlyassigned spatial units are 10 out of 12 or 83.3%. In the second and thirdcategories (medium and high deforestation) the correctly assignedcases are 6 out of 10 and 9 out of 15 respectively. As regards the finalcategory of very high deforestation, the model assigned correctly100% of the cases. Therefore, the overall predictive ability of themodelis very high especially at the first and four categories.

4. Results and discussion

Table 5 presents the parameter estimates, standard error, Waldstatistic, significance level and the lower and upper bound of 95%confidence interval. Overall, the estimates indicate that population-related variables and indicators of economic activity had a stronginfluence on forest resources during the ‘90s in Greece. Yet, theunfoldment of the particular relationships has created a highlycomplex pattern.

In particular, the regression coefficient for “Urban Sprawl” (X1)appears to have a negative sign for the first and second category;however, the level of statistical significance is satisfactory only for thefirst category. The negative sign of the first category indicates that theprefectures with low values of urban sprawl are much less likely topresent high deforestation rates than the prefectures where urbansprawl is high. In other words the results indicate that high rates ofdeforestation are associated with high values of urban sprawl.Therefore, the diffusion of urban land uses outside the formal townand city boundaries had a negative effect on forests. An increase inurban sprawl can result either directly to the decrease of forest landdue to encroachment of such areas for building housing units orthrough the deterioration of natural environment due to wildfires orother detrimental acts. Urban sprawl, usually, increases land valuesand population density creating the necessary conditions for forestland use conversion and modification.

However, the ordinal regression model has an interaction termbetween urban sprawl and the explanatory variable depicting“change in hotel beds”. The coefficient of the term is statisticallysignificant (p-value=0.053) for the first category and therefore,simple and interaction effects of the model should be interpretedtogether. Bearing in mind that, the actual influence of urban sprawlon deforestation rate should be estimated be using the followingmathematical equation:

∂Y∂X1

= β1 + γ2* X9 ð9Þ

Appendix B contains the results of the calculation for the actualrelationship between deforestation and urban sprawl under theinfluence of the variable that depicts changes in tourism accommo-dation infrastructure in each prefecture. In regions where both urbansprawl and the change in tourism accommodation infrastructure arelow, the regression coefficient has a negative sign meaning that theseareas are less likely to have their forest resource depleted than theareas with high urban sprawl and a low rate of change in tourismaccommodation. However, there is a turning point in the rate ofchange in tourism accommodation infrastructure (value of 1.38) overwhich the regression coefficient changes sign and becomes negative.This means that the regions with low urban sprawl but with highincrease in tourism accommodation are more likely to present a high

Table 4Confusion matrix.

Rate ofDeforestation

Predicted response category Total

Low Medium High Very high

Low N 10 1 1 0 12% 83.3%a 8.3% 8.3% 0.0% 100.0%

Medium N 1 6 3 0 10% 10.0% 60.0%a 30.0% 0.0% 100.0%

High N 3 2 9 1 15% 20.0% 13.3% 60.0%a 6.7% 100.0%

Very high N 0 0 0 14 14% 0.0% 0.0% 0.0% 100.0%a 100.0%

Total N 14 9 13 15 51% 27.5% 17.6% 25.5% 29.4% 100.0%

a Percentage of correctly assigned cases by the model.


deforestation rate than the areas with high urban sprawl. In theseareas (e.g. Zakynthos), despite the fact of low urban sprawl during thestudy period, the growth in tourism infrastructure seems to beunsustainable in respect to forest resources. Generally speaking, it canbe said that low urban sprawl is associated with low deforestationrate. However, when this low sprawl is mostly due to tourism growth,then the deforestation rate raises. It seems that the pattern of tourismgrowth during the study period was not sustainable as long as forestareas are concerned. Yet, it is not clear whether the new tourismaccommodation infrastructure was directly developed on forest landor its negative effects on environment were indirect.

Coming to the second level of urban sprawl (i.e. medium) we canobserve the same pattern. The regions with medium sprawl are lesslikely to present high deforestation rates that the regions with highurban sprawl. However, when medium urban sprawl is combinedwith extensive growth in tourism infrastructure then these areas aremore likely to have their forest depleted. The above results indicate aqualitative feature of urban sprawl. Tourism-related urban sprawlseems to be more harmful to forests than the general sprawl of urbanactivities.

Table 5Parameter estimatesa.

Estimate

Thresholdω1 Forest land use change=[1] −62,591ω2 Forest land use change=[2] −59,108ω3 Forest land use change=[3] −53,204

LocationΧ1 Urban sprawl=[1] low −24,182– = [2] medium −7771– = [3] high 0a

Χ2 Geographical character= [1] (mainland) −18,220

– = [2] (insular) 0a

Χ3 Change in rural population −28,973Χ4 Indirect population potential 0.247Χ5 Urban plan expansion 0.892Χ6 New legal housing activity 0.604Χ7 Prosperity level −0.406Χ8 Change in GDP in agriculture −0.533Χ9 Change in hotel beds −14,943Χ10 Informal housing activity 1826

2-ways interactionΧ4⁎Χ10 Indirect population potential⁎ informal housing activity −0.061Χ1⁎Χ9 Urban sprawl=[1]⁎change in hotel beds 17,547– Urban sprawl=[2]⁎change in hotel beds 7376– Urban sprawl=[3]⁎change in hotel beds 0a

Link function: Cauchit.a This parameter is set to 0 because it is redundant.

As regards the variable that depicts the geographical character ofregions, we can see that the relevant regression coefficient has anegative sign for mainland regions. This means that the mainlandregions are less likely to present a high deforestation rate than theinsular ones. It seems that at least during the study period, insularforest areas received greater pressure for changing their use or theywere proved more vulnerable in such changes. Environmentallyspeaking, this is extremely important if we take into account that theinsular regions in Greece are low forest cover areas. In the case ofislands, the relative scarcity of natural resources such as fresh water,fertile cultivated areas and available open land combinedwith the lowresistance of Mediterranean costal forest ecosystems to respond toexternal pressures, pose certain constraints to the types of develop-ment that the insular regions could sustain. The potential foreconomic growth through the expansion of existing and theestablishment of new firms is close-related to the existing land uses,to the total available space and to the applied development policies. Insuch spatial units, the stages proposed by forest transition theory maynot be applicable. In a small or medium-sized spatial unit, when therate of deforestation is high and the detected land use changes aremostly irreversible (e.g. forest to urban uses), then the available timeto policy makers and experts to counteract is limited.

The third variable that depicts changes in rural population duringthe study period has a negative coefficient and a p-value of 0.036. Thenegative sign indicates that as the population of rural areas increasesthe likelihood of getting high rates of deforestation decreases. It seemsthat rural rebound processes in Greece are associated with lowdeforestation rates. It could be said that this is logical if we considerthat shortly after the 1983 major urban planning reforms in Greece(Law 1337/1983), there were massive extensions of the existingformal boundaries of villages as well as small and medium-sizedtowns. The new available urban land contributed towards bothlowering the pace of urban sprawl as well as creating a real estatemarket of affordable developable land plots. These results lead us tothe conclusion that the growth of population in the numerous smalland medium-sized settlements in Greece does not pose serious

Std.error

Wald df Sig. 95% confidence interval

Lower bound Upper bound

25,704 5929 1 0.015 −112,970 −12,21124,595 5775 1 0.016 −107,314 −10,90222,705 5491 1 0.019 −97,704 −8,704

11,690 4279 1 0.039 −47,094 −12706000 1678 1 0.195 −19,530 3988. . 0 . . .

6968 6837 1 0.009 −31,877 −4563. . 0 . . .13,810 4402 1 0.036 −56,041 −19060.114 4719 1 0.030 0.024 0.4700.454 3851 1 0.050 0.001 17830.255 5627 1 0.018 0.105 11030.192 4461 1 0.035 −0.782 −0.0291183 0.203 1 0.652 −2852 17865988 6227 1 0.013 −26,680 −32060.790 5337 1 0.021 0.277 3374

0.024 6333 1 0.012 −0.109 −0.0149075 3739 1 0.053 −0.239 35,3334710 2452 1 0.117 −1856 16,608. . 0 . . .


problems on the conservation of forest resources. It is worthmentioning that the definition of non-urban population by the NSSGrefers to those people who live in settlement of a total population ofless than 10,000 individuals. The density of buildings within thesesettlements is usually low, and there is also sufficient space formeeting future demand for housing. Furthermore, the prices ofdevelopable land are relatively medium or even low. Therefore, anydemand for housing due to an increase in population can be satisfiedwithin the current boundaries of town plans. In such cases, there arenot intense pressures on forest land stock.

The coefficient of Indirect Population Potential (X3) has a positivesign and it is also significantly different from 0 (p=0,030) indicatingthat this variable is strongly related to the observed changes in forestland uses during the study period. Accessibility of each prefecture inrelation to the rest of the prefectures does seem to be an importantcausal factor of deforestation. The positive sign indicates that as theindirect population potential increases so do deforestation rates.Therefore, the prefectures situated in the vicinity of large urbanconcentrations seem to be more prone to extensive forest land usechanges. This tendency is usually associated with the construction ofsecondary or vacation housing units by the dwellers of large urbancentres to adjacent prefectures. Fig. 3 is a graphical representation of therelationship between deforestation and per capita vacation houses perprefecture multiplied by a constant of 100. It is apparent that asdeforestation rates increase so does themedian of the variable depictingvacation houses. It is also evident that the spread of values around themedian drops as deforestation increases.

However, in the ordinal regression model, it was included aninteraction term between indirect population potential and informalhousing activity. Bearing in mind this, the actual influence of indirectpopulation potential on deforestation rate should be estimated be usingthe following mathematical equation:

∂Y∂X4

= β4 + γ1* X10 ð10Þ

Appendix B contains the results of the calculations for the actualrelationship between deforestation and indirect population potentialunder the influence of the explanatory variable that depicts themagnitude of informal housing activity in each prefecture. As we cansee, in region with very low informal housing activity the influence ofindirect population potential on deforestation has a positive sign. Thismeans that in areas with low informal housing activity, as accessibilityimproves deforestation increases. However, in regions where infor-mal housing is relatively high having a value above 4 buildings per1000 people, then the relationship between indirect populationpotential and deforestation becomes negative. The negative sign ofthe coefficient means that in those regions as the population potentialincrease, deforestation decreases. The abovementioned relationshipneeds be more thoroughly examined and therefore we constructFig. 4.

The Y axis represents informal housing activity whereas the X axisdepicts the values of indirect population potential. The size of thecyclical symbol for each prefecture is analogues to the magnitude ofdeforestation in that prefecture. According to the total regressioncoefficients (simple effect and interaction effect), it appears that for theprefectures above the red line the relationship between deforestationand accessibility (i.e. indirect population potential) is that of diagramA.This relationship suggests that in regionwhere informal housing is high,as accessibility improves deforestation decreases. On the other hand, forthe prefectures below the red line, the relationship between accessibil-ity and deforestation is depicted by diagram B. According to thisdiagram, in regions with low informal housing activity, as accessibilityimproves deforestation increases. Taking into account the spatial

configuration of the abovementioned relations as presented in diagramsC and D, we can make the following comments:

• Most of the prefectures where an A-type relationship is in place (i.e.negative association between accessibility and deforestation) aresituated in the vicinity of Athens, Thessalonica and Patra, the threelargest urban centers of the country. On the other hand, most of theprefectures where a B-type relationship is in place (i.e. positiveassociation between accessibility and deforestation, are situated atsome significant distance of major urbanized areas.

• It appears that in prefectures of high accessibility (e.g. Korintia,Viotia, Pieria), where informal housing is also high, the rate ofdeforestation has started declining. It is possible that in these areas,forest land use changes occurred during previous periods and atpresent, deforestation is declining implying a trajectory analogousto the one suggested by the later stages of forest transition theory.However, this conclusion needs to be empirically tested byanalyzing deforestation statistical data for the 70s and 80s.

• On the other hand, it is likely that the most remote prefectures oflower informal housing activity are presently at a stage of being“discovered” by the urban populations. It appears that in thoseregions as accessibility gets improved, the rate of deforestationaccelerates. According to the results, these regions might be at someinitial stage of forest transition theory.

Next, the variable “Urban Plan Expansion” (X5) has a positive signand the relevantp value is 0.050 indicating that the null hypothesis of noactual relationship between this independent variable and the observedchanges in forest resources can be rejected. The positive sign of thecoefficient implies that the prefectures with extensive expansions oftheir urban boundaries are more likely to present high deforestationrates compared to the ones that urban boundaries expansions werelimited. Bearing in mind that planning urban expansion directly onforest areas is prohibited by the constitution, it could be argued that theabovementioned expansions were made in order to organise andpossibly legalise extensive areas of already constructed both legal andillegal urban developments. In this case, urban planning does not seemto follow a proper integrated demand and supply managementapproach. Land uses are determined largely by market principlesinstead of coherent urban planning efforts. These results indicate thatthe current urban planning system cannot be considered sustainable, atleast as long as forest resources are concerned.

The indicator of “legal housing activity” (X6) in each prefecture has apositive and statistically significant coefficient. The positive sign meansthat the regions with increased legal housing activity are more likely topresent high deforestation rates. As it was hypothesized earlier, newlegal housing activity cannotdirectly affect forest resources in anegativeway due to institutional prohibitions. Therefore, it is possible thatdespite of the fact of being legal on institutional terms, this particularhousing activity has not been sustainable threatening forest resourcespossibly through indirect detrimental effects.

As regards the indicator “Prosperity level” (X7), the regressioncoefficient has a negative sign and it is also statistically significant(p=0,035). The negative sign indicates that the regions with higherprosperity level are more likely to present lower deforestation ratescompared to the regions of low prosperity level. These results are inaccordance with the arguments of forest transition theory which statesthat, in the long run, as the prosperity level improves, deforestationdecreases. However, in the context of the current model, the observedlow deforestation rate in prosperous prefectures could be a temporarytendency. In order to arrive to a safer conclusion it is necessary toanalyse the relevant data concerning several previous decades.

In order to determine any likely linkages between agriculturalactivity and forest losses, there was introduced into the analysis thevariable “Change in the GDP in agricultural sector” (X8). The relevantcoefficient of the model reveals a negative association with thedependent variable. However, the level of statistical significance is not

Fig. 3. Boxplot of per capita vacation houses multiplied by a constant of 100 per prefecture in relation to deforestation rate.


satisfactory and therefore we cannot reject the null hypothesis that thisparticular regression coefficient is 0. Hence, we do not have enoughevidence to conclude neither for the existence of a relationship nor forthe likely character and magnitude of it.

The variable representing “Changes in hotel beds”(X9) is negativelyassociated with the rate of deforestation and the observed level ofsignificance is 0.013. The negative sign indicates that the regions withextensive changes in their tourism accommodation infrastructure areless likely to experience high deforestation than the regions with lowchange in their tourism infrastructure. This means that tourismdevelopment during the study period did not have a significantdetrimental effect on forest resource in tourism orientated regions.However, there is an interaction effect between “Change in hotel beds”and “Urban sprawl” that we should take into consideration. Treating“Change in hotel beds” as the focal independent variable and “Urbansprawl” as themoderator variable, we can see that the effect of the focalindependent variable on the dependent varies as a function of themoderator variable. The results of the calculation are presented inAppendix B and they have been estimated be using the followingmathematical equation:

∂Y∂X9

= β9 + γ2* X1 ð11Þ

Taking into account the above mentioned interaction contrast wecan say that when urban sprawl is low (having a value of 1) theregression coefficient of the effect of “Change in hotel beds” ondeforestation, has a positive sign (+2,604). This means that the regionswhere urban sprawl is relatively low and at the same time the change in

their hotel beds is high are more likely to present high deforestationrates than the regions with both high urban sprawl and high change intheir hotel beds infrastructure. Spatially speaking, most of the newtourism accommodation infrastructure follows a scattered pattern andit is usually located in ex-urban areas (sprawl development). Therefore,it seems that when urban sprawl is relatively low but it is mostly due tonew tourism infrastructure then there is a detrimental effect of thattourism infrastructure on forests. These results reveal a certain trend asregards deforestation and tourism infrastructure. Several low urbansprawl regions are remote, less developed area. These areas have startedto attracted significant investments in tourism infrastructure because oftheir unspoiled natural environment. However, the host areas have nothave inplace a coherent spatial plan for receiving tourismactivities in anorganisedmanner and developing a sustainable tourism sector. At theseinitial stages of tourism development the emerging land use patternsare the product of strong influence of market forces. Environmentalconsiderations and planning issues are underestimated and as a resultthe impacts on forest resources seem to be significant.

Finally, the variable representing “Informal housing activity” (X10)has a positive coefficient and the observed significance level is 0.021.The positive sign means that in regions with increased informalhousing activity, the likelihood of having higher deforestation is largercompared to the regionswith low informal housing activity. It appearsthat there are significant adverse impacts on forest resources due toinformal housing phenomenon. Again, this is the simplemain effect ofthe ordinal regression model. As long as there is a statisticallysignificant interaction effect between informal housing and indirectpopulation potential, we should take it into consideration. Appendix Bpresents the results of the calculations of the effect of informalhousing (as a focal independent variable) on deforestation under the

Fig. 4. Graphical representation of the relationship between deforestation and accessibility under the influence of informal housing activity.

469D.M

inetos,S.Polyzos/Forest

Policyand

Economics

12(2010)

457–472


influence of the moderator variable of indirect population potential.The calculations have been based on the following formula:

∂Y∂X10

= β10 + γ2* X4 ð12Þ

Taking into account the above mentioned interaction contrast wecan make the following comments:

• In prefectures with relatively low indirect population potential (e.g.Dodekanisos) the sign of regression coefficient for informal housingis positive implying that as informal housing activity increases sodoes deforestation.

• In prefectures with relatively high indirect population potential (e.g.Arkadia, Evia) the sign of the b-coefficient is negative meaning thathigh informal housing activity is associated with lower deforesta-tion rates.

Spatially speaking, the results indicate a varying relationshipbetween deforestation and informal housing activity. The areas thatare not very close to large urban concentrations are more likely toexperience deforestation due to informal housing whereas the areasin the vicinity of urban centres are less likely to have their forestsdepleted. This is an odd but interesting spatial pattern which mayimplies a qualitative difference in informal housing activity acrossspace. In particular, a likely explanation for this pattern is thatnowadays, in areas located in the vicinity of large urban concentra-tions, informal housing activity takes the form of “soft” constructionillegalities (i.e. building more stores and space on the ground than thebuilding licence permits, building on land plots that are not of asufficient size to be buildable as this is stated in the relevant planningacts, changing the use of existing buildings to illegal uses etc.). Inthese cases, forest recourses are not affected negatively, at least in adirect way. On the other hand, in more remote areas, it appears thatinformal housing activity is significantly associated with forest landuse changes. In these areas, construction illegalities are “hard” innature and they include the direct (illegal) change of the use of forestland to urban. The unspoiled natural environment of these areasattracts investment interest in the form of tourism or second houseinfrastructure. As a result, land prices change and the whole areas getsto a state of unplanned development. This particular process ofdevelopment is unsustainable and environmentally harmful resultingto the degradation of forest ecosystems. However, the aforemen-tioned patterns need a further, in-depth analysis and evaluationsupported by relevant data sets.

5. Conclusions

This paper has dealt with the likely factors of forest land usechanges in Greece for the period from 1990 to 2000. The impacts ofeach driving factor on forest land are evaluated by using ordinalregression, a methodology that has not been used as often aspolychotomous logistic regression or other types of multivariatestatistical models in land use studies. Effective methodologies forassessing sustainability of land use planning and investmentallocation decisions on a regional scale are vital. This methodologyhas demonstrated that can copewith low quality land use change dataand this is an advantage.

Making informed forest policy decisions is central to achievingsustainability at a regional level. Prior to formulating certain sustainablepolicy objectives and targets, the baseline information which is neededis the kind of driving forces that influence current forest land usepatterns. Generally speaking, these driving forces are closely associatedwith the economic, social and environmental context within which theregions exist and function. The effects on forest land of the predictorvariables that were employed by this study, while significant in mostregions are still covered with many uncertainties. Some theoretically

interesting explanatory variables have indicated that the effects ofcertain processes on land use changesmay be important but not alwaysstraightforward.

A synthesis and evaluation of the results brings up some importantissues relevant to the theoretical framework of the empirical analysis. Inparticular, a noticeable argument relates to the course of developmentof deforestation rate in the long term,when themajor competinguses toforests are urban and not agricultural land uses as it is assumed byAngelsen's (2007) model in the case of tropical deforestation. It seemsthat when the antagonism involves urban and forest land uses and alsothe types of forest ecosystems fall into the category of not-productiveforests (as it is the case for most Mediterranean forests) then land-rentgenerated by forest uses is unlikely to compensate for the one comingfrom urban development of the land. Urban land uses through marketmechanisms will tend to outrage forests. As long as the Greek law isstrongly opposed to the conversion of forest, it seems that, at least in theshort term, the only way of confronting this market-induced processmight be the restructuring of the law enforcement mechanisms.

On the other hand, it seems that inmost urban prosperous regions aswell as in their vicinity, deforestation has slowed down, althoughadditional data need to be analysed. Deforestation moves to moreremote regions as accessibility improves. The empirical analysis hasshown that there are interactions amongst indirect population potentialand illegal housing activity that influence the spatial distribution ofdeforestation rate. This complex relationship implies a geographicaltransfer and dispersion of illegal housing phenomenon from urbanizedregions to remote, less-urbanized ones. It is likely that urban popula-tions remain the major source of illegal housing activity; however theynow tend to exercise this activity in longer distances due to accessibilityimprovements. If this is the case then deforestation controllers are stillin urban areas even though the impacts of their acts are beingsystematically “exported” to other areas. The geographical transfer ofdeforestation relieves forests in the places of origin but at the same time,it escalates pressure on the host areas’ forests.

An additional issue, relevant to the abovementioned argument,relates to the geographical characteristics of the areas being deforested.In insular prefectures, land surface is a scare resource and areas coveredwith forests are limited. If such spatial units were involved in a lengthyurban-forest land use antagonism, it would probably be difficult for allstages of forest transition theory to take place. Due to geographicalremoteness, small-sized insular regions cannot easily base part of theirdevelopment on exploiting the land of adjacent regions. Thus, it isdifficult for islands to “export” deforestation processes. Bearing in mindthe scarcity of developable land, high rents associated with urban landuses, the lack of forest cadastral maps and the insufficient forest lawenforcementmechanisms, it ismore likely for insular forests tomove tothe direction proposed by the theory of the tragedy of commons ratherthan the one proposed by forest transition theory. Even if the country asa whole managed to increase its forests, there would probably bewinners and losers at the regional scale.

Generally speaking, the results indicate that the spread of urban usesin the countryside has negatively affected forests either directly orindirectly during the study period. The improvement of transportationinfrastructure, the expansion of urban plans, urban sprawl, illegalhousing activity and legal building construction activity create acomplex negative background for forests land uses. On the other hand,changes in GDP in agriculture and change in tourism infrastructureeither have limited or no adverse impacts on forests. In the context ofthis particular empirical analysis, it seems that the observed improve-ment in the performance of agricultural sector in the relevant regionshas not happened to the expense of forests through some expansion ofcultivated land. In addition, new tourismaccommodation infrastructure,possibly due to mandatory environmental impact assessment intro-duced in the early 90s, had limited negative effects on forests. However,regions with high growth in their tourism accommodation in ex-urbanareas show significant signs of deforestation.

(continued)

(b) Estimation of the impact of indirect population potential on deforestationunder the influence of informal housing activity

Prefecture(example)

Total regression coefficient Kind ofrelation

Etoloakarnania β4-total=β4+γ1⁎Χ10=+0.247+(−0.061⁎4)=0

Turning point Χ10→4

Chalkidiki β4-total=β4+γ1⁎Χ10=+0.247+(−0.061⁎6)=−0.119

(−) 4bΧ10b14.7

Chios, Evia β4-total=β4+γ1⁎Χ10=+0.247+(−0.061⁎14.7)=−0.649

(−) 4bΧ10b14.7

(c) Estimation of the effect of changes in tourism accommodation infrastructure ondeforestation under the influence of urban sprawl

Prefecture(example)

Total regression coefficient Kind of relation

Sign Interval

Kind of relation

Appendix B (continued)


In the context of planning a sustainable forest policy, accessibilityissues as well as the spatial patterns generated by urban phenomenasuch as urban sprawl and illegal housing are of crucial importance. Atfirst glance, the improvement in the regional level of prosperity mightbe associated with lower deforestation rate. However, it is not certainwhether this additional prosperity has been achieves in a sustainablemanner. It is likely that improvement in prosperity at some location hasbeen achieved in the expense of forests at some other location. Theseresults could guide further research for improving understanding onspatial processes such as forest land use changes and for rationalizingforest-related decision making. Strategic project monitoring andappraisal as well as evaluation of project impacts on land uses canhelp land planners and land use decision-makers to introduce specificenvironmental protection objectives into land development andplanning processes.

Appendix A
Imathia β9-total=β9+γ2⁎Χ1=−14.943+
17.547⁎1=+2.604(+) Χ1=1

Evia β9-total=β9+γ2⁎Χ1=−14.943+7.376⁎2=−0.191

(−) Χ1=2

(d) Estimation of the impact of informal housing activity on deforestation underthe influence of indirect population potential

Prefecture(example)


Sign IntervalDodekanisos β10-total=β10+γ1⁎Χ4=+1.826+

(−0.061⁎3)=+1.643(+) Χ4→3

Zakynthos β10-total=β10+γ1⁎Χ4=+1.826+(−0.061⁎9)=+1.277

(+) 3bΧ4b30

Preveza β10-total=β10+γ1⁎Χ4=+1.826+(−0.061⁎30)=0

Turning point Χ4→30

Arkadia, Evia β10-total=β10+γ1⁎Χ4=+1.826+(−0.061⁎67)=−2.261

(−) 30bΧ4b107

Summary statisticsa.

Codes Intervalsand cut-points

N Marginalpercent

Cumulativepercent

Rate ofdeforestation1991–2001

[1]=low 0.86+ 12 23.5% 23.5%[2]=medium 0.81–0.85 10 19.6% 43.1%[3]=high 0.71–0.80 15 29.4% 72.5%[4]=very high b=0.70 14 27.5% 100%

Urban sprawl [1]=low b=3.00 15 29.4% 29.4%[2]=medium 3.01–5.00 17 33.3% 62.7%[3]=high 5.01+ 19 37.3% 100%

Geographicalcharacter

[1]=mainlandprefecture

Nominalscale

37 72.5% 72.5%

[2]=insularprefecture

Nominalscale

14 27.5% 100%

Valid 51 100.0% –

Missing 0 – –

Total 51 – –

aLink function: Cauchit.

Appendix B

(a) Estimation of the impact of urban sprawl on deforestation under the influenceof changes in tourism accommodation infrastracture

Prefecture(example)


Urban sprawl=low=[1] Sign IntervalKorintia β1-total=β1+γ⁎Χ9=−24.182+

17.547⁎1=−6.535(−) 0.86bΧ9b1.38

Kerkyra β1-total=β1+γ2⁎Χ9=−24.182+17.547⁎1.38≈0

Turning point Χ9→1.38

Zakynthos β1-total=β1+γ2⁎Χ9=−24.182+17.547⁎2.67=+22.668

(+) 1.38bΧ9b2.67

Urban sprawl=medium=[2] Sign IntervalDrama β1-total=β1+γ2⁎Χ9=−7.771+

7.376⁎0.83=−1.648(−) 0.83bΧ9b1.05

Larisa β1-total=β1+γ2⁎Χ9=−7.771+7.376⁎1.05≈0

Turning point Χ9→1.05

Magnesia β1-total=β1+γ2⁎Χ9=−7.771+7.376⁎1.36=+2.260

(+) 1.05bΧ9b2.10

(b) Estimation of the impact of indirect population potential on deforestationunder the influence of informal housing activity

Prefecture(example)


Sign IntervalSamos, Florina β4-total=β4+γ1⁎Χ10=+0.247+

(−0.061⁎0)=+0.247(+) Χ10→0

Larisa, Drama β4-total=β4+γ1⁎Χ10=+0.247+(−0.061⁎2)=+0.125

(+) 0bΧ10b4

(continued on next page)

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Deforestation processes in Greece: A spatial analysis by using an ordinal regression model

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