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Land Use Policy 35 (2013) 257– 270

Contents lists available at SciVerse ScienceDirect

Land Use Policy

jou rn al hom epage: www.elsev ier .com/ locate / landusepol

ssessment of the renewable energy-mix and land use trade-off at aegional level: A case study for the Kujawsko–Pomorskie Voivodship

eata Sliz-Szkliniarz ∗

uropean Institute for Energy Research (EIFER) at the KIT, Emmy – Noetherstr. 11, 76131 Karlsruhe, Germany

a r t i c l e i n f o

rticle history:eceived 21 November 2011eceived in revised form 22 May 2013ccepted 23 May 2013

eywords:

a b s t r a c t

Renewable energy sources (RES) can undoubtedly contribute to protecting the environment and con-serving fossil fuels, as well as enhancing regional and rural development opportunities. However, everyenergy production process affects the environment and involves the use of land resources. The riskslinked to intensified RES use should be adequately taken into consideration in any planning process, asill-conceived energy policies may adversely impact land and local ecosystems, and lead to increases in

ISenewable energy sourcesiomassind

olarand-use

public spending. Therefore, before designing any instruments for the regulation of both RES and land-use,the most essential step is to explore investment possibilities in different contexts. This paper intends tolocate and quantify the potentials of biomass, wind and solar as well as to explore some of the potentialplanning issues associated with their development. The methods and findings presented in this papermay help to build a vision for the development of an optimal RES portfolio and to highlight emergingproblems associated with RES deployment.

© 2013 Elsevier Ltd. All rights reserved.

ntroduction

Over the past few decades, economic development has beenccompanied by an intense increase in energy production, whichas resulted in a depletion of fossil resources and has neg-tively affected the environment. Alternative energy resourcesave therefore increasingly gained in importance as a meanso tackle the problems mentioned above. Despite their manyenefits, renewable energy sources (RES) are not without envi-onmental and socio-economic impacts. Compared to conventionalossil fuel-based energy systems, renewable energy sources are

ore space-intensive and their efficiency of energy production isighly geographically dependent (Seager et al., 2009; Dijkman andenders, 2010). The limited availability of land as a production fac-or is a restraining element in the production of energy, food andther non-food goods. Thus planning at a regional level plays a keyole in balancing competing interests for land resources and in man-ging the multiple uses of land (Helming et al., 2008). The methodsnd findings presented in this paper may help to build a vision forhe development of an optimal RES portfolio and to highlight some

f emerging problems associated with RES deployment.

∗ Tel.: +49 0721 6105 1443; fax: +49 0721 6105 1332.E-mail address: sliz@eifer.uni-karlsruhe.de

264-8377/$ – see front matter © 2013 Elsevier Ltd. All rights reserved.ttp://dx.doi.org/10.1016/j.landusepol.2013.05.018

RES targets and status-quo in Poland and in theKujawsko–Pomorskie Voivodship

Poland has set ambitious goals for renewable energy production.According to the Polish National Renewable Energy Action Plan(NREAP) published in 2010, wind energy and biomass are expectedto meet 47% and 44% of total electricity production (NREAP, 2010).The document “Development of agricultural biogas plants in Polandin 2010–2020” published by the Council of Ministers on 13 July2010 (Ministry of Economy, 2010), promotes biogas production.This sets an objective of increasing biogas power capacity in Polandfrom 82 MW (including the capacity of 8.4 MW of 8 agricultural bio-gas power plants constructed by the end of 2010) to 2000 MW by2020.

According to Kus and Faber (2009) around 2.1 Mha of farmland(22% of the arable land in Poland) should be dedicated to energygeneration to fulfill the biomass targets. It is designated accord-ingly: 0.5 Mha of good quality soil for biodiesel production based onoil seed rape, 0.6 Mha for bioethanol (cereals, sugar beets, potato),around 0.5 Mha for the production of solid biomass and, in addition,around 0.5–0.7 Mha for biogas development.

The Kujawsko–Pomorskie Voivodship is characterized by a highindex of arable land per capita of 0.48 ha compared to the national

average being 0.36 ha/cap, suitable pedoclimatic conditions andstrong potential for animal by-products (K-PBPPiR, 2010). Accord-ing to the Polish Agency for Restructuring and Modernization ofAgriculture, which provided statistics on the growing area of energy

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edicated crops, 52,408 ha in 2009 of the arable land in Polandere used to grow energy crops (ARiMR, 2010). About 10% of theseational plantations were located in the region.

With respect to other types of alternative energy, the case studyegion is the third most favorable region for to wind regime condi-ions (Lorenc, 2005). The favorable wind and land conditions makehe region suitable for the development of wind projects. By the endf the year 2010, 155 wind turbines with a total power capacity of66 MW were in operation in the region (URE, 2010a).

In Poland, photovoltaic (PV) power still lacks the economicower to compete with other utilization technologies (ECBREC

EO, 2009). So far, around 1 MWp of photovoltaic power has beennstalled (URE, 2010a). However, an upcoming amendment to theertificate system intends to enhance PV development (Ministry ofconomy, 2011).

Poland is still at the beginning of a challenging path to meethe energy targets. RES development should be systematically andarefully addressed using financial and spatial policy instrumentso enhance the development of sustainable energy systems basedn locally available resources.

ntroduction to renewable energy development and land-userade-offs

Economic and demographic growth has resulted in increas-ng demand for land use (Helming and Pérez-Soba, 2011). Limitedvailability of land means that a balanced portfolio of social, eco-omic, and environmental services are needed (Wiggering et al.,006). Accordingly, different RES options compete with each otheror land as well as competing with other uses of land, for examplegriculture, leisure and ecological conservation.

Wind energy production has higher land use efficiency (energyield per unit of land used) than biomass and solar energy. More-ver, unlike with energy crop production or solar farms, the landnder a wind farm can be used for other purposes, such as agri-ulture, because wind turbines occupy only a fraction of a farm’surface. However, this type of energy generation may have adversempacts on ecologically sensitive areas and on the esthetics of theandscape, thus affecting conservation and recreation (Krewitt anditsch, 2002).

Unlike wind parks, ground mounted PV systems only have amall negative impact on ecosystems, but they are incompatibleith most other uses of the land (Tsoutsos et al., 2005; Günnewig

t al., 2006; Chiabrando et al., 2009). The most crucial issue associ-ted with solar power parks, highlighted by Tsoutsos et al. (2005),s the reduction of cultivable land. Chiabrando et al. (2009) sug-est that solar farms should only be allowed if a building integratednstallation is not economically viability or energy efficient. Accord-ng to Dijkman and Benders (2010) the competition for arable landould be reduced if large PV parks were located on marginal ground,ot suited to agricultural production. This would ensure there is noompetition with food and biomass production. However, otherompeting needs for land use cannot be excluded.

By the same token, biomass energy production may affect landesources in other ways because it requires land for crop cultiva-ion, storage and generation (Russi, 2008; Dijkman and Benders,010). Furthermore, intensive biomass production may deplete soilutrients thus affecting the soil’s productivity, and may contributeo the loss of biodiversity (Huston and Marland, 2003; Robertsont al., 2008; Sala et al., 2009). Biomass production not only conflictsith food and fodder production, but also with other energy crops.

or instance, biomass may be used in different technologies result-ng in electricity, heat or biofuels. Annual and perennial crops fornergy or commercial material production compete for space withonventional plants.

licy 35 (2013) 257– 270

All energy-related projects always result in some environmen-tal burden. Most of them are associated with fragmentation of thecountryside, visual impact on landscape and interference with floraand fauna (Chiabrando et al., 2009). The expansion of alternativeenergy sources is a compulsory EU target so its deployment mustbalance the multiple uses of land. The development of an optimalRES-mix requires administrative guidance and appropriate incen-tives (Directive 2009/28/EC). At the same time, regulations shouldnot discourage investment in RES, and growth should enhance thebenefits to environmental and socio-economic systems. Therefore,before designing any regional or local RES policy and regulationinstruments, the most essential step is to explore the investmentpotential of RES.

Objective of the study

Research has been conducted to assess the potential of renew-able energy sources, to identify geographic and socio-politicalbarriers to development and to explore the potential effects of RESon socio-economic systems and on the environment (Kaltschnittand Hartmann, 2001; Hoogwijk, 2004; BFE, 2007; EEA, 2007;Berndes et al., 2008; Hoogwijk and Graus, 2008; Krewitt et al., 2008;Stangeland, 2008; Iglinski et al., 2009; Dees, 2010; Bronner, 2011).However, these studies do not offer an approach to evaluating thepotential of the three main alternative resources: biomass, windand solar in the context of the competition for land-use and envi-ronmental burdens. This study locates and quantifies the economicand technical potential of biomass, wind and solar. In addition, itexplores some of the planning issues associated with their potentialdevelopment. The approach was designed on a regional scale.

Methods and materials

Dataset

Supported by Geographic Information System (GIS) – ArcGIS9.3 – the spatial analyses used the Corine Land Cover CLC 2006data at a 1:100 000 scale (IGIK, 2009) and the datasets at a 1:750000 scale, representing land-use (e.g. transport infrastructure, wet-lands, forestlands etc.) obtained from the Office of Spatial Planningof the Kujawsko–Pomorskie Voivodeship (KPBPP, 2009).

Approach for analyzing the potential of renewable energy-mix

In a liberalized market, land is in hands of private investorsand land-users, who look after their own interests. This oftenleads to competing renewable energy options and conflicting useof land. For this reason, the development of sustainable energyresources needs suitable guidance and management by admin-istrative authorities, in order to balance the actions of privateinvestors with the public interest. At the same time, regulationshould not discourage potential investors and RES growth shouldbenefit both environmental and socio-economic systems. There-fore, before designing any incentives or instruments of regulation,the first step must be to explore the investment possibilities ofrenewable energy sources.

The choice and deployment of RES is a complex issue. Marquesand Fuinhas (2010, 2011) made empirical assessments of severalsocio-economic and political determinants that encourage and/orhamper the deployment of renewable energy. Alongside legal, tech-nical and economic factors, there are social factors that influencethe realizable potential of RES (Zoellner et al., 2008). Acceptance

may be expressed in various forms: attitudes, behavior and – mostimportantly – investments (Wüstenhagen et al., 2007).

The focus of this study is on investors and the potential of invest-ment in wind, biomass and solar energy. The analysis was carried

B. Sliz-Szkliniarz / Land Use Policy 35 (2013) 257– 270 259

g inve

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Fig. 1. Local factors influencin

ut on the basis of several local factors (see Fig. 1) in order to explorenvestors’ or land-users’ decisions on the potential deployment ofenewable energy.

An investor or a land-user tends to make decisions on invest-ent options on the basis of available information such as land

onservation policy, including nature and landscape conservationnd the availability of infrastructure (e.g. power lines, roads). Fur-hermore, land quality is a decisive factor for biomass projects andffects crop production and investment profits. Other factors suchs energy and crop yield, profitability of energy generation options,nd demand for biomass and for renewable energy influence theconomic viability of RES projects.

Some information that investors might want is simply notvailable for example future demand for, and price of, a prod-ct. Therefore a high risk investment such as short rotation cropsSRC), perennial grass crops or biogas plants, would need to beffset by higher profits compared to other conventional cropsr RES investment options. However, the extent of appropri-te compensation for these investment risks, through financialupport schemes, is also hard to monetize, given that the land-sers’ willingness to take risks is subjective. If investment supportchemes are overestimated, it might result in adverse direct, or

ndirect, socio-economic effects such as a long-term burden onlectricity consumers (Frondel et al., 2010) or an increase in landease (Kiessling and Lingenfelser, 2011). However, if these sup-ort schemes take too narrow a view, they will either fail to

stors’ decision on RES options.

enhance RES development or RES expansion will lag behind itstargets, as has been happening in Poland. Therefore, a flexible setof policy instruments and support measures needs to be tailoredto regionally specific conditions (Michalena and Hills, 2012). Pol-icy aspects (including energy policy, its instruments and incentivemeasures) coupled with a minimum of medium-term continuity,can provide sufficient certainty for investors (Marques and Fuinhas,2010; Michalena and Hills, 2012).

Several empirical studies have identified a set of factors thatshape public attitudes toward renewable energy. The most relevantfactors that influence multi-faceted public opinion are policy, envi-ronment, economics, the impact on landscape, local perception, andsocial influence (Gross, 2007; Jobert et al., 2007; Van der Horst,2007; Zoellner et al., 2008). Both social acceptance and the valueplaced on traditional local farming are relevant drivers in determin-ing the success of RES deployment. People who derive a positivesense of identity from rural landscapes are likely to oppose RESprojects (Van der Horst, 2007). Public acceptance in a national orregional context is generally high, but decreases as it moves towardthe local level (Wüstenhagen et al., 2007). Community accep-tance plays a key role in influencing investor decisions. However,a key challenge is to obtain social acceptance of renewable energy

development on a local level (Wüstenhagen et al., 2007; Zoellneret al., 2008). Jobert et al. (2007) shows that local acceptance ofstakeholders is influenced by both planning rules and site-specificfactors.

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ssessment of biomass energy potential

This section provides an approach for exploring the technicalnd economic potential of biogas. In addition, the economic feasi-ility of crop production was studied on the basis of a gross margin

ndicator.

eographical potential of biogas feedstockThis study provides a means to evaluate the potential for, and the

eographic distribution of, biogas feedstock (animal manure andelected crops) on a regional scale and to determine appropriateites for biogas development under ecological, technical and eco-omic criteria. Constrained areas were defined in accordance withhe Nature Protection Act (Ministry of Environment, 2001). Bufferones were created around built-up areas, water and protectedreas, roads, railways, wetlands and forestlands (see Appendix 1).dditional buffers of 2 km were established around the power net-ork and the natural gas. Next, to create a map illustrating potential

ones, an iterative process was used. Initially, 41 zones, charac-erized by density of animal manure within a 10 km radius, weredentified. When the density of manure in these areas droppedelow 200,000 m3, the iterative process was interrupted. Withespect to the economies of scale, the study looked at 215 dairyarms and 317 pig farms operating with a minimum of 100 livestocknits (ECBREC IEO, 2004; FNR, 2009). These three individual mapsere overlapped to extract a final map, which identified potential

ones for biogas development.The optimal mix of biogas feedstock depends on the choice of

ermentation system and is influenced by economic criteria andhe availability of biogas feedstock. In this study, wet fermentationystems were considered with a total dry matter (DM) of 15%. This isainly based on animal slurry with added co-substrates to increase

he amount of organic material thus achieving a higher gas yieldBraun et al., 2009; Weiland, 2010). The required amount of cropilage was calculated assuming wet fermentation with 15% DM ofhe total biogas feedstock-mix.

echnical and economic potential of biogas productionThe assessment of the technical and economic potential was

arried out for energy generated in combined heat and power (CHP)lants. Biogas production is proportional to the dry and organicatter content of co-substrates and can be calculated as follows:

S =∑

Si ∗ DMi ∗ VSi ∗ MVSi [m3] (1)

here BS is the annual biogas production in m3; Si is the amountf substrates i [t/d]; DMi is the dry matter in the substrate i [%]; theolatile solids VSi is the concentration of organic matter in the totalolid of substrate i [%DM]; MVSi is the biogas content in the VSi ofubstrate i [m3/t VS].

Biogas yield was calculated under the assumption that cattlennually produce 15 tons of manure per LSU and pigs 14 tons perSU (Steppa, 1992; Kaltschmitt et al., 2009).

The linear regression curve fitting to data derived from the studyy Urban et al. (2009) was plotted and used to calculate the electri-al power output of biogas CHPs, based on the hourly biogas volumetream:

P = 1.8543 ∗ BS [kWe] (2)

here EP is the electrical power output of CHP in kWe and BS is theourly biogas volume stream in m3/h.

The power regression of best-fit data derived from literatureFNR, 2006; Urban et al., 2009; KTBL, 2010) was used to estimate

he investment costs (IC) of anaerobic digesters from the biogasow as follows:

C = 14239 ∗ BS − 0.2209 [ε/m3h] (3)

licy 35 (2013) 257– 270

where IC is the total investment cost of an anaerobic digester in D ;BS is the hourly biogas volume stream in m3/h.

The investment costs of the Otto gas engine was estimatedfrom the power regression plotted for the data (FNR, 2006), whichdepicts the correlation of the electrical power output with specificinvestment costs as:

ICOE = 3814.8(EP) − 0.2916 [ε/kWe] (4)

where IOEC is an investment cost of the Otto gas engine in D /kWand EP is the electrical power in kW.

The operation and maintenance costs associated with feeding,operating and repairing the plant, as well as with the storage ofco-substrates were defined as a constant and size-independentfraction of investment costs at 0.04 for an anaerobic digester and0.03 for an Otto engine (DBFZ, 2009).

The annual costs of energy generation were estimated based oncomponents of the investment costs annualized over the economiclifetime of a given plant. The unit cost per Megawatts hour wasestimated as follows:

Cn = Cf + Cc + OM + A + (IC + ICOE) ∗ a ∗ s

Ee + Eh[ε/MWh] (5)

a = i(1 + i)n

(1 + i)n − 1(6)

where Cn is the annuitized energy production cost in D /MWh; Cf isthe cost of biogas feedstock in D ; Cc is the connection cost in D ; Ais the amortization cost in D ; OM are the annual operating costs inD ; IC and ICOE are investment costs of an anaerobic digester andan Otto gas engine; a is the annualized rate; s is the subsidy to thequalified investment costs in D ; Ee and Eh are the electrical and heatenergy generated per year in MWh; i is the interest rate in % and nis the economic lifetime of the plant.

A 20-year lifetime was assumed for all the equipment and anaverage amortization rate of 7% of investment costs was assumedover a period of 15 years. The annuity factor was calculated over 15years at interest rates of 4 and 10%.

Revenue earned from electricity and heat production is com-posed of the market price of electricity and heat in addition to theprice of the tradable set of certificates of origin. The unit revenueswere calculated as:

Re = En ∗ PE + Hn ∗ PH + (En + En ∗ 0.09) ∗ Pcg + Ee ∗ Pcy

Ee + Eh

[ε/MWh] (7)

where Re is the annual revenue in D /MWh; En is the net energy fedinto the power grid, depending on the electrical power in MWh;Hn is the purchase price of net heat in D /MWh; Pcg is the price ofgreen certificate in D and Pcy is the price of yellow certificate in D .

In 2011, the minimum sale price for electricity guaranteed bythe Polish Energy Regulatory Office amounted to 45 D per MWh.By feeding heat into the local district heating network, producersmay increase their income by 8 D /GJ (2.3 D /MWh) (URE, 2010b).Beside this, producers of green electricity also acquire a green cer-tificate (Pcg) that was traded for 68 D /MWh. Since the amendmentto the Polish Energy Law of 1 March 2010, an additional benefit– yellow certificates – may be combined with green certificates.The yellow certificate of origin is awarded for electricity generatedthrough high-efficiency cogeneration processes, regardless of thepower capacity of agricultural biogas. It was assumed that around9% of the electricity and 25–40% of the heat used for processesrelated to biogas production (FNR, 2006; MAE, 2009).

Economic potential of energy dedicated cropsLand availability limits the production of agricultural crops for

bioenergy, food or other uses. A farmer’s decision on what crops

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o grow will thus not only depend on the production costs andield potential of a given crop, but also on its profitability for bioen-rgy production, food and other non-food uses when compared tother crops (Möndel, 2008). The study evaluated alternatives forrop production on the basis of a gross margin indicator that allowsor the annuitization of costs and revenues of crops grown over aumber of years, as follows:

0 = −I0 +n∑

t=1

(rt − ct)

(1 + i)t[ε/ha] (8)

= C0 ∗ i(1 + i)n

(1 + i)n − 1[ε/ha] (9)

here C0 is the capital value at the beginning of investment in D /ha;0 is the initial investment cost in D ; rt is the direct income in theear t in D ; ct represents the variable costs in the same year t in D ;

the interest rate; a is the annuity factor and n is the length of thealculation period.

The gross margin calculation was made on the basis of thenvestment and variable costs specified in literature (Kwasniewski,008; Matyka, 2008; Faber et al., 2009). Cultivation costs includeosts related to farming operations: planting, fertilizing, harvest-ng and recultivation. For the willow crop, a 21-year plantationifespan with an even, average yield after a 3-year rotation period

as assumed. Sida and miscanthus grass are usually harvested eachear over a 20-year cycle.

In practice, prices for the dried mass of perennial crops oscillateround the average price for wood chips. In this study, the averageurchase prices of biomass at 50 D /t and 72 D /t were used to calcu-

ate the gross margin of perennials and SRC compared to the grossargin of conventional crops.

ssessment of wind energy potential

This section provides an approach for exploring the technicalnd economic potential of wind power.

ind energy sitingThe evaluation of wind energy potential is achieved through a

et of steps incorporating the technical and geographical character-stics of a region. Land-use functions, represented by digital layers,

ere processed in the GIS model according to recommended guide-ines (Kubicz et al., 2003; Hailer et al., 2004; Juchnowska and Olech,006). Appendix 2 lists the criteria applied as well as suggested dis-ances to areas of ecological sensitivity, infrastructure, wetlands,orestland and cultural components. Land specified as “landscapereas” have protected status but with less stringent restrictions onevelopment and economic use than are applied to national parks.herefore, within such areas, wind turbine construction may not betrictly excluded. Such exceptions may be even permitted withinhe Natura 2000 network (Marcinkowski and Sztuba, 2009). In thenal step, ecosystem and landscape conservation areas were thusanked according to their sensitivity to adverse impact, as outlinedn Appendix 3.

echnical and economic potential of wind energyOnce the land for wind power development was identified and

lassified, its technical potential was assessed. The wind speed

ataset derived from weather stations was horizontally and ver-ically interpolated to derive the continuous surface of wind speedt a rotor blade height of 100 (Sliz-Szkliniarz and Vogt, 2011). Then,he generated wind energy was estimated for a turbine of 2.5 MW

licy 35 (2013) 257– 270 261

based on the Rayleigh probability distribution function and a powercurve. The power curve was calculated following the formula:

Pn = 12

q ∗ A ∗ C ∗ V3 (10)

where A is the rotor diameter in m; C is the curve of rotor efficiencyfor wind speed intervals of 1 m/s; V is the mean wind speed intervaland q is the air density in kg/m3.

The Rayleigh probability density function is given by:

f (V) = �

2

(V

Vm2

)exp

[−�

4

(V

Vm

)k]

(11)

where Vm is the average wind speed in m/s; k is the shape param-eter; V is the wind speed interval.

The annual wind energy yield (E) was calculated by multiplyingthe wind power curve (Pn) with the intervals in frequency distri-bution of wind speed f(V) as follows:

E = 8760t=n∑t=1

Pnf (V) [MWh] (12)

Finally, to evaluate the economic viability of wind power develop-ment, unit costs and revenues were estimated. The average annualcost per kilowatt-hour of electricity generated by a wind turbinewas derived from the sum of total annual investments, the oper-ating costs and the turbine’s annual energy yield. The unit cost ofenergy was calculated using the following formula:

CEi = I ∗ P ∗ ROM + a ∗ I ∗ P

Ei[ε/MWh] (13)

a = i(1 + i)n

(1 + i)n − 1(14)

where CEi is the cost of 1 kWh of electricity generated in a grid cell iin D /MWh; I is the initial investment cost depending on the turbinesize P in D /MW; Ei is the energy yield per grid cell i in MWh; n isthe economic life time of the turbine (20 years); ROM is the rate ofoperation and maintenance costs; i is the interest rate and a is theannuity factor.

Operation and maintenance costs were assumed to repre-sent a constant rate of 0.03 of the investment cost over thelifetime of the installation (Hoogwijk et al., 2004). The averageinvestment cost in wind power was estimated for a range of1000 D per kW–1600 D /kW (Baj, 2009; EEA, 2009; EWEA, 2009;Marcinkowski and Sztuba, 2009). The total price earned by windenergy producers is composed of the market price of electricity at45 D for each MWh and the price of the tradable green certificatesat 68 D /MWh, which comes to 113 D /MWh in total.

Assessment of solar energy potential

This section provides an approach for exploring the technicaland economic potential of ground-based photovoltaic (PV) systems.

PV sitingThe evaluation of sites for land-based photovoltaic system con-

struction is achieved through a set of steps that incorporate theGIS-based analyses of slope and orientation and land-use functions.Free-standing PV systems installed on inclined land, cause extracosts due to construction difficulties and shadows effects (Ademeand Axenne, 2009). Taking into account the technical aspects ofsolar energy use and the optimal tilt angles for different PV arrays,

the conditions for the slope and orientation were determined asoutlined in Appendix 4. In order to enhance sustainable PV develop-ment, PV parks should primarily be constructed on favorable sitesunsuitable for agricultural purposes (e.g. bare rocks, burnt areas,

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ump sites). Appendix 5 shows the land-use classification basedn CLC 2006 data. Furthermore, possible sites for the installationf PV systems include agricultural areas covered by orchard plan-ations that offer the chance to convert the area into arable landt a later date. Unlike orchards, it is difficult to adapt forestlands,rban areas and wetlands to solar power constructions, and there-ore they are identified as unsuitable sites as indicated in Appendix. Areas with land-use functions like biodiversity conservation and

andscape protection were divided into three classes, according tohe degree of protection they have been given: high (areas pre-luded from PV development), moderate and low (see Appendix). Unlike wind parks, ground-based PV applications are not veryompatible with farming, thus agricultural land quality was alsossigned three levels of constraints: high, moderate and low.

echnical and economic potential of solar energyThe profitability of a photovoltaic project is determined by elec-

ricity production, its price and the availability of financial supportchemes. The electricity production costs of PV systems used inhis study were calculated on the basis of investment costs (both

odule cost and the Balance of System) and operational and main-enance costs for two options: rooftop and ground applications. Thennual costs of solar electrical energy generation were estimatedsing the following formula:

EPV = a(M + BoS) + ROM(M + BoS) + L

E ∗ pc[ε/kWh] (15)

here a is the annuity factor estimated as in case of wind energysee Eq. (14)); L is the annual land lease in the case of free-standingV units; M are the investment costs of PV modules; BoS are costs ofll components of PV systems apart from the module (e.g. inverter,lectrical cabling, electrical protections and array support struc-ure); ROM is the rate of operation and maintenance costs, whichre a fraction of the total investment costs; E is the annual gener-ted electricity; and pc is a mean yearly performance degradationoefficient assumed at 0.5% (Aste and Del Pero, 2010).

In the study, the investment costs of module and BoS weressumed at 3 D per Wp in the case of roof-mounted installationsnd at 2 and 2.5 D per Wp for a ground-based PV applicationLenardic et al., 2009). A cost of photovoltaic energy was calcu-ated at a discount rate of 4 and 10% and a 20-year system lifetime,ssuming annual maintenance costs of 1% of the total investmentosts. The operating and maintenance costs including insuranceees were assumed to amount to 1.3% of the PV hardware costs.s for wind energy, the sales price for PV electricity was assumedt 113 D /MWh.

ssessment of ground-based solar and wind power potential andand-use trade-offs

Sites of potential conflicts between the erection of wind parks,round-based PV applications and land quality used for growingrops were evaluated. The siting conditions and constraints out-ined in Appendices 3–6 were simultaneously evaluated throughhe conditional formula in the Map Algebra in ArcGIS 9.3.

esults

The following sections illustrate the potential of the threeenewable energy sources in the context of some of the planningssues associated with their development.

otential of regional renewable energy-mix

The technical potential of biogas, wind and ground-based solarnergy production is summarized in Table 1. The study finds 41

Fig. 2. Energy generated by turbine of 2.5 MW.

potentially suitable sites for biogas development. By adding cropsilage (e.g. maize, rye) to animal waste, the biogas yield increasesfrom 24 to 98 Mm3. Correspondingly, the total energy generatedfrom biogas feedstock increases fourfold to 953 GWh.

Assuming a power density of 1 MW per 10 ha, the preliminaryassessment finds 62 GW of wind power potential (see Table 1).Approximately 620,000 ha of unconstrained land could theoreti-cally be dedicated to wind farms. Corresponding energy potentialand site classifications for wind park development are illustratedin Figs. 2 and 3.

The average annual solar radiation on optimally inclined sur-faces is slightly below the national Polish average and rangesbetween 1140 and 1180 kWh/m2 (JRC, 2010). At the optimumregional inclination of 33◦, the total annual electricity generatedby crystalline silicon systems is 843 kWh/kWp with a south-facingexposure to the sun, whereas the performance of west or east-facing plants drops by 20% (PVGIS 2006). Favorable sites for theerection of large freestanding PV parks are mines and dump sites,and bare rocks. With respect to sustainability criteria, arable land ofhigh and moderate quality should be protected from construction,although PV development is not excluded. However, land-users arefree to determine the use of their land, as long as it falls withinregulatory constraints.

When considering only two site classes; favorable sites and lowquality land outside protected natural areas, an area of 141,568 hacould theoretically be dedicated to ground-based solar farms.

To identify potentially suitable sites for wind and solar powerdevelopment under low and moderate site constraints, a localimpact assessment must be conducted. The regional study shownhere allows for identifying sites and zones. The next step is the localanalysis, which is based on a better quality of digital maps, socialacceptance and then local planning.

Potential of biogas feedstock and land demand for biogasdedicated crops

The study investigated the potential for competition for landbetween crops grown for energy, food or fodder. This theoretical

B. Sliz-Szkliniarz / Land Use Policy 35 (2013) 257– 270 263

Table 1Summary of the potential of biogas, wind and solar energy and associated development constraints in the Kujawsko–Pomorskie region.

Biogas energy

Origin Methane [Mm3] Gross energy [GWh] Area [haa] Constraints

Only animal manureLivestock units > 100 24 226 – Decrease of animal populationFeedstock-mix15% DM (LSU > 100) 98 953 22,000 Costs of co-substrate, investment costs

Wind energy

Potential siting on Area [ha] Power capacity [MW] Gross energy [GWh] Constraints

Unconstrained sites 619,016 61,902a 123,803 AcceptabilityLow protection areas 11,508 1151a 2302

Solar energy

Potential siting on Area [ha] Power capacity [MWp] Gross energy [GWh] Constraints

Favorable sites 1115 372b 316b Investment costs,support system,acceptability

Low quality land and outside protected areas 140,453 46,818b 39,795b

yield o

ao

rtavifbatm

Land demand for energy crops and land-use trade-offsTo identify potential demand for farmland dedicated to other

energy crops in the region, it was assumed that the 2020 national

a Assuming the power density of 1 MW per 10 hab Assuming the PV system area performance of 1 MWp per 3 ha and solar energy

pproach aims to provide insight into a potential risk of pressuren the land arising from the demand for biomass.

Biogas-dedicated crops would take up to 2.2% overall of theegion’s arable land in order to supply the 41 AD plants identified inhe study. This assumes an average yield of 35 t/ha of crop silage onn area of 22,000 ha. To explore the competitive pressure on con-entional agricultural production, the agricultural area was definedn catchment areas of 5 and 10 km radiuses from the potential sitesor biogas plants. The demand for land depends on the quantity of

iogas co-substrates required for biogas plants and on the avail-bility of agricultural land. When feedstock production is limitedo a 5 km radius around biogas plants, the supply of energy crops

ay require in some cases to use up to 39% of the arable land (see

Fig. 3. Site classification for wind parks development.

f 850 kWh/kWp

Fig. 4). When biogas crops are collected within a 10 km distance, thedemand for feedstock can be met by considerably lower competi-tive pressure on conventional agricultural production. As shown inFig. 5, the demand for biomass would take up to 11% of the arableland in the catchment area of 10 km.

biomass targets would be transferred to the region on the basis

Fig. 4. Share of arable land dedicated to biogas production in total agricultural landin a radius of 5 km of biogas plants and power capacity.

264 B. Sliz-Szkliniarz / Land Use Policy 35 (2013) 257– 270

Fi

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bcaatwutrstslMaimwoae

a

ig. 5. Share of arable land dedicated to biogas production in total agricultural landn a radius of 10 km of biogas plants and power capacity.

f a reference factor – the share of a regional agricultural land inhe national area of farmland, which is 7%. Under this hypotheticalssumption that 7% of biomass would be grown in the region inrder to cover proportionally the national biomass targets by 2020,and used for the generation of bioenergy (e.g. biodiesel, ethanol)

ould amount to around 112,000 ha. This is 7% from the area esti-ated by Kus and Faber (2009) (see chapter 1.1). This simplified

pproach results in a theoretical demand for 7% of land surface toe used for cultivating crops for ethanol and biodiesel production,nd 2.2% of land for biogas identified in this study.

Crop farming of both energy and conventional crops can exacer-ate pressure on land-use so it is necessary to examine the possibleonflict between protecting the land and using it. To achieve this,rable land was evaluated for the need for conservation as wells for existing risks of soil erosion and water scarcity. The cul-ivation of invasive alien species (i.e. miscanthus, sida, robinia)ithin biodiversity and landscape conservation areas is restrictednder current law (Ministry of the Environment, 2004). In addi-ion, planting willow and poplar could also be constrained withineclamation areas, as the roots may damage the irrigation infra-tructure (MINROL, 2007). Correspondingly, the study finds thathe cultivation of perennials in the Kujawsko–Pomorskie Voivod-hip is legally restricted to within a 266,763 ha area of protectedand, which covers 26% of the total regional arable land available.

oreover, the cultivation of those crops should be avoided in anrea of 287,089 ha (equaling 28% of the total arable land availablen the region), which is characterized by water scarcity. Intensive

aize cropping should be avoided on areas affected by soil erosion,hich cover 87,027 ha. An area of 428,818 ha (corresponding to 41%

f the total arable land available in the region) is free of any of theforementioned restrictions and would be suitable for cultivating

nergy crops.

Fig. 6 shows arable land evaluated against nature conservation,reas of erosion and potential water scarcity occurring over the

Fig. 6. Arable land situated within areas affected by soil erosion and protectionconstraints.

growth period. This allows a better understanding of the scatteredregional constraints and risks in conventional and energy crop pro-duction.

Areas of sensitive biodiversity should be kept free from mono-culture or intensive production. Furthermore, in respect to the riskof increased ground water pollution and soil erosion, it is not rec-ommended to grow maize crops within water protection areas orwithin zones of featuring soil erosion from water, which accountfor around 25% of the total regional cropland. Therefore, a differ-entiated crop-mix is recommended to mitigate possible soil andwater conservation risks in the future (EEA, 2007).

Assessment of ground-based solar and wind power potential andland-use trade-offs

This study finds sites of potential conflicts between the erec-tion of wind parks, ground-based PV applications and land qualityused for growing crops. Photovoltaic parks may be located on highquality arable land unless there are environmental constraints.Thus, the potential siting of solar farms on moderate and highquality arable land might inflame conflicts in agricultural produc-tion. Consequently, in these agricultural areas, the development ofwind power should be given priority over solar power. In order toenhance sustainable RES development, large stand-alone PV sys-tems should primarily be constructed on favorable sites, outsidenature conservation areas, and characterized by low quality farm-land outside any protected natural areas.

Table 2 outlines the area of site classes. In theKujawsko–Pomorskie Voivodship, land without environmen-tal and infrastructural constraints that is designated for wind parkdevelopment, and does not conflict with PV systems would cover

a surface of 9178 ha (see Wind-NC in Table 2). Sites covering 5%of the region’s total area, are not high quality land and do nothave any environmental constraints on their use (see Wind-NC

B. Sliz-Szkliniarz / Land Use Policy 35 (2013) 257– 270 265

Table 2Site classification for PV systems and wind parks in the Kujawsko–PomorskieVoivodship.

Site Site classification Area [ha] Share [%]

1 Wind-NC 9178 0.512 PV favorable 1087 0.063 PV-NPL 48,822 2.734 Wind-NC and PV-favorable 28 0.005 PV-LL 4010 0.226 Wind-NC and PV-NPL 91,608 5.137 Wind-NC and PV-LL 14 0.008 Wind-NC preferred against PV 439,670 24.629 Wind-L and PV-NPL 23 0.00

10 Wind-L and PV-LL 2625 0.1511 Wind-L preferred against PV 6149 0.34

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12 Wind-L 518 0.0313 Other sites 1,182,177 66.19

nd PV-NPL in Table 2). Here there may be possible competitionetween solar and wind energy development.

Correspondingly, Fig. 7 illustrates the final site classificationor wind and solar power development, where sites classified as:

ind-NC indicate a possible unrestricted location for wind devel-pment that is also suitable for PV parks. Sites identified as Wind-NCnd PV-Favorable are locations suitable for the development ofoth competing energy sources. There are identified constraintsssigned to wind farms, and the locations are also favorable for solararks. PV-Favorable means sites located on dump sites or mineralxtraction sites. PV-NPL indicates sites suitable for the construc-ion of solar parks and located outside ecologically protected areaselated to this installation. At these sites, the environmental and/ornfrastructural constraints either exclude the construction of windurbines or have a high or moderate level of ecological protec-ion. PV-LL means sites that have been identified in areas under

low level of ecological protection and are on low quality farm-and. Wind-NC and PV-LL indicates sites with the same conditionsor solar farms as the previous one, but with no constraints on windurbine construction. Wind-NC preferred against PV means that loca-ions are within areas of high or medium quality agricultural land,nd thus wind farms would be favored over large PV parks. Wind-Lnd PV-NPL are sites identified within areas of low levels of protec-ion with regard to wind farms, but have none of the ecological ornfrastructural constraints for PV parks and they have low qualityrable land. Wind-L and PV-LL are sites located within areas thatave a low level of ecological protection for wind and solar farmsnd are on low quality farmland. Wind-L preferred against PV meansocations within areas of high and medium quality agricultural land,ut with a low level of nature conservation. Nonetheless, the erec-ion of wind turbines should be preferred to PV park development.

ind-L are sites under low level of nature conservation and are notuitable for PV systems (Fig. 7).

conomic potential of regional renewable energy-mix

The following sections exemplify the economic outcomes of thehree renewable energy sources.

conomic potential of biogas productionThe economic study of biogas production shows that this

nvestment option in Poland can be profitable. To enhance theevelopment of biogas plants, various forms of funded subsi-ies from regional, national sources have recently been launchednd many others are planned (IPiEO and ARR, 2010; Ministry of

conomy, 2010) in addition to the existing preferential loans andnergy tax incentives. Assuming a subsidy of 50% on the investmentn AD and CHP equipment, plants of 2 MW and more can make arofit even without purchasing yellow certificates (O2), however

Fig. 7. Site classification for the development of PV systems and wind parks in theKujawsko–Pomorskie Region.

this can only be achieved with an purchase price at 20 D per ton ofcrop silage (see Table 3). With the increased price for cereal silageof up to 30 D /t, only a few biogas projects could generate profitfrom selling electricity and heat, as well as green and yellow certifi-cates (O3). The calculation made for both options, with and withoutsubsidies, at 4% and 10% return of debt shows that the economicviability of a biogas plant largely depends on the acquisition priceof biogas feedstock. In addition, without the co-generation (yel-low) certificate support, the majority of biogas projects would beunprofitable, even when including a subsidy of up to 50% of eligiblecosts.

Economic potential of energy cropsIn this study, the annualized profits were calculated for low,

average and high yield per hectare of willow (Salix viminalis), mis-canthus (Miscanthus sacchariflorus), sida (Sida hermaphrodita),rapeseed, winter wheat, winter rye, and grain maize (see Table 4).The gross margin of annual crops was calculated for both options:with and without the direct payment (DP) awarded to farmers bythe Agency for the Restructuring and Modernization of Agriculture.The subsidy payment for perennial and short rotation crops (SRC)and establishment subsidies were only granted up to 2009.

Gross margin for SCR and perennials vary according to changesin purchase price. Given a purchase price of 72 D /t for fresh biomass,these crops can be economically competitive with annual crops (seeTable 4).

Excluding the subsidy for annual crops, the gross marginof perennial and SRC at a purchase price of 50 D /t competeswith maize and winter rye, which only bring profits with highyields. Oilseed rape achieves high profits even without subsidies.

266 B. Sliz-Szkliniarz / Land Use Policy 35 (2013) 257– 270

Table 3Annual costs and revenues of biogas energy production (O1, purchase price of electricity and green certificate, O2, purchase price of electricity, heat and green certificate,O3, purchase price of electricity, heat, green and yellow certificates).

Subsidy [%] 0 50 0 50 Revenues [D /MWh]Return of debt [%] 4 10Substrate cost [D /t] 30 20 30 20 30 20 30 20

Electricity [MWh] Electrical power [kW] Costs [D /MWh] O1 O2 O3

1950 250 70 62 64 56 75 67 67 59 45 46 583900 500 66 56 61 51 70 60 64 54 48 49 637800 1000 60 50 56 46 64 54 58 48 49 49 63

15,600 2000 58 48 54 44 61 51 56 46 50 51 6523,400 3000 55 45 51 42 58 49 53 44 53 53 68

Table 4Gross margin of selected crops according to crop yield (DP – direct payment).

Crops Purchase price Annual yield Gross margin [D /ha]

Minimum Average Maximum

Willow 72 D /tfm 10, 14, 18 t/ha 138 272 41150 D /tfm 10, 14, 18 t/ha 20 106 197

Miscanthus 72 D /tfm 12, 18, 24 t/ha 24 248 47750 D /tfm 12, 18, 24 t/ha −124 26 181

Sida 72 D /tfm 11, 16, 22 t/ha 55 239 46850 D /tfm 11, 16, 22 t/ha −81 41 196

Winter wheat 140 D /t incl. DP 4, 5.5, 7 t/ha 236 351 476140 D /t excl. DP 4, 5.5, 7 t/ha 20 135 260

Maize 140 D /t, incl. DP 5, 6.5, 8 t/ha 101 179 257140 D /t, excl. DP 5, 6.5, 8 t/ha −115 −37 41

Rapeseed 300 D /t, incl. DP 2, 3, 4 t/ha 158 336 494

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300 D /t, excl. DP 2, 3, 4 t/ha

Winter rye 90 D /dt, incl. DP 2, 3.5, 5 t/h90 D /dt, excl. DP 2, 3.5, 5 t/h

owever, due to rotational constraints related to yield perfor-ance, rapeseed crops can only be grown in the same location once

very four years. Therefore, the choice of optimum crop produc-ion for the crop rotation period determines the overall opportunityosts based on a 20-year crop rotation plan.

With respect to the moderate land requirements of biomassrops, relatively high levels of production can be achieved at thoseocations where average yields of wheat and rapeseed, as well as aigh harvest of rye and maize, are expected (Faber et al., 2009). Aseen in Table 4, the gross margin of perennials and SRC with highield potential lies above the gross margin reached with averageroduction of annual plants. The economic assessment illustrateshat farmers could choose to invest in willow, sida and miscanthusrops, especially on sites characterized by moderate quality land.

conomic potential of wind energyThe monetization of technical wind potential revealed that the

ujawsko–Pomorskie Voivodeship offers favorable economic con-itions for investors because of the level of incentives.

The profitability of wind power, depending on investment costsnd energy yield, is outlined in Table 5.

Comparing the lowest wind energy generation cost of8 D /MWh with the total purchase price of 113 D /MWh, windrojects seem to be very attractive for potential investors. Even athe maximum level of investment of 1600 D /kW, but with a loweriscount rate of 4%, energy production costs would range from7 D /MWh to 82 D /MWh depending on the annual wind energyield. Assuming the highest investment cost of 1600 D /kW andn interest rate of 10%, a wind project would not be economicallyiable.

conomic potential of solar energyIn this study, costs of electricity generated by photovoltaic sys-

ems were calculated to give insight into the economic viability of

−58 120 27986 191 298

−130 −25 83

large ground-based PV projects in comparison to roof-mounted PVprojects.

In Poland, investment in solar electricity has been encouragedby investment subsidies, preferential loans and tax allowances, aswell as with tradable green certificates. Polish investors are eligiblefor grants worth up to 60% of their investment costs.

The economic study finds that, with an investment range of2.0 and 2.5 D /Wp for large ground-based PV parks, developmentrequires the additional support of subsidies in order to be prof-itable (see Table 6). An investor could only consider a large PVpark as part of an investment portfolio with subsidized investmentcosts at a level of 2000 D /kWp. Due to investment costs higher thanthat of large PV parks, rooftop photovoltaic applications seem to beunprofitable. As shown in Table 6, the annual costs are higher thancurrent annual revenues.

From an economic point of view, investment in solar energycurrently appears less attractive than investment in wind energy orbiogas plants. Nevertheless, the economic potential of solar energygenerated by ground mounted PV systems shows that solar projectsin this region could be economically feasible; at first with supportschemes and over time because of decreasing investment costs.

Economic potential of renewable energyWind energy offers investors encouraging economic potential

due to the quota system (certificate system). Despite this, actualpotential will depend on localized, specific criteria (i.e. an environ-mental impact assessment) and the acceptance of stakeholders tointegrate these installations into the local energy systems.

In respect of solar electricity targets under current conditions,low potential for return on investment is the main barrier toimplementing free-standing photovoltaic systems or even rooftop

applications. However, this situation may change, as the invest-ment costs of PV systems has been systematically decreasing whichwill result in investment in solar energy generation becoming morecompetitive (Chiabrando et al., 2009; EPIA, 2010; Dinc er, 2011).

B. Sliz-Szkliniarz / Land Use Policy 35 (2013) 257– 270 267

Table 5Annual costs and revenues of wind energy production.

Wind power [MW] 2.5Investment costs [D /kWp] 1000 1600Loan of total investment [%] 100Return of debt [%] 4 10 4 10

Costs [D /MWh]Electricity production[MWh/y]

5033 51 73 82 1175368 48 69 77 109

Revenues [D /MWh]113

Table 6Annual costs and revenues of energy generated by 10 MWp and 10 kWp PV systems.

PV power peak 10 MWp 10 kWp

Investment costs [D /kWp] 2000 2000 2500 2500 2000 2000 2500 2500 3000Loan of total investment [%] 100 80 100 80 100 80 100 80 100 80 100 80Return of debt [%] 4 10 4 10Subsidy [%] 60 0 60 0 60 0 60 0 60 0 60 0Land lease [D /ha*y] 400 –

Costs [D /MWh]Energy 800 100 174 125 217 144 261 179 326 144 292 210 424[kWh/kWp] 850 94 163 117 204 136 246 169 307 136 275 198 399

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Revenues [D /MWh]113

Perennial energy crops must be grown on moderate quality lando generate a good yield and become more profitable than annualnergy crops. On the other hand, the current difference betweenhe gross margins of the different crops might not be enough with-ut supplementary payments over a period of 20 years, to offsetconomic risk (Faber et al., 2009). This situation may change veryoon with an expected decrease in initial investment costs (Pawlak,009) and an increase in the price of lignocellulosic biomass driveny a growing demand for agricultural biomass.

Investor risk associated with the cultivation of annual energyrops is no different to that of annual conventional crops. The profitargin depends on annual prices offered either on the food market

r by biofuel producers. The competitiveness of biofuel produc-ion has so far relied on support mechanisms (e.g. tax incentives,ayments to energy crops, certificates etc.). This market is highlyependent on policy decisions, which will in turn affect investors’ecisions.

Production of biogas crops is contingent upon the demand foriogas development, which is also related to a set of quota certifi-ates. Up to March 2010, the Polish Government’s aid mechanismthe green certificate) favored the cheapest RES technology – windurbines. Subsequently, the diversification of quota-based mecha-isms released new investment options, predominantly in biogaslants. However, these investment options still carry the signifi-ant risk associated with the unpredictability of income sourcesrom certificate systems. This income is not guaranteed over thentire lifetime of the biogas plant, however, the upcoming Renew-ble Energy Law to increase financial support of biogas and biomassnergy generation through the incentive system will guarantee 15-ears of support over the economic life time of the technologyMinistry of Economy, 2011).

In Poland, inconsistence and short-term political and economiconditions are still major barriers to RES development.

iscussion and conclusion

In the region of Kujawsko–Pomorskie, the interesting potentialf the wind and biomass (incl. biogas) energy was revealed. In theontext of land efficiency, wind energy production seems to be bet-er option when compared to biomass and solar energy. However,

this type of energy affects conservation and the landscape and hasmore opponents than solar and biomass energy projects.

The scenario that free-standing PV parks can contribute toland-use conflicts in the Kujawsko–Pomorskie Voivodship cannotbe excluded in the future and therefore compensatory measureswould be required. On the other hand, the synergy effect achievedthrough the dual use of land for instance settlements and energyproduction can be realized by means of roof-mounted solar sys-tems. However, the rooftop PV deployment requires the additionalsupport of subsidies in order to be profitable. Eventually, if the highcosts of generating solar energy are to be compensated by upcom-ing amended incentive measures, this might impose high costs onsociety.

Due to a high rate of agricultural land per capita inKujawsko–Pomorskie compared to the whole country, the cul-tivation of other energy crops is unlikely to affect food supplyindependence over the medium-term. However, the study showedthat a potential risk of pressure on the land may arise from thedemand for biomass within the catchment areas defined for thebiogas-dedicated crops.

This study explores not only the technical and economic poten-tial of alternative energy production, but it contributes to a deeperunderstanding of how different RES production options competewith each other for land resources and with other uses of land.The findings demonstrate that Polish investors have enjoyed greatfreedom in decision-making on land use, and that this could lead tofuture spatial disorder from uncontrolled expansion of renewableenergy production. The study reveals that the development of a sus-tainable RES-mix is not guaranteed under the current frameworkof legal instruments and support mechanisms. The study demon-strates that the mix of energy is very dependent on individual sitesand that the transformation of national objectives onto regionaland local levels requires careful analysis.

The methods and findings presented in the paper may helpto develop an optimal RES portfolio based on regionally avail-able resources. However, these results are not enough on whichto base a decision on the appropriate RES-mix. The eventual deci-

sion emerges from social discourse colored by varying degrees ofacceptance of alternative energy sources. The aspect of the com-munal acceptance of RES was beyond the scope of this study andrequires further empirical survey on a local scale.

2 Use Po

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68 B. Sliz-Szkliniarz / Land

Correspondingly, the transparent approach outlined in thisaper would also help to build public acceptance of an optimalegional energy portfolio that balances the interests of stakehol-ers.

cknowledgments

I am very grateful for the financial support for this PhD researchranted by the German Academic Exchange Service (DAAD). I wouldike to express my gratitude to Prof. Joachim Vogt for the help-ul suggestions in the course of the PhD thesis and my thanks toundula Marks for her advice and comments on an earlier ver-ion. I would like to express my thanks to the Spatial Planningffice in Kujawsko–Pomorskie Voivodeship, to the Institute of Soilnd Science and Plant Cultivation and to the Agency for Restruc-uring and Modernization of Agriculture for providing the digitalatabase.

ppendix 1. Exclusive and selective criteria for biogasnfrastructure development sites

Distance [m]

Exclusive criteriaForests –Water bodies 50Floodplains 50Water protection areas –Natura 2000 network, ecological corridors, landscape parksand areas, nature reserves,

–

Built-up areas 300Roads, railways 10

Selective criteriaPower grid 2000Gas grid 2000

ppendix 2. Constraints for wind turbine siting in theegion Kujawsko–Pomorskie

Distance [m]

SettlementsResidential area 500Single dwellings 500Industry and commercial development zone 250

Leisure time and green areasLeisure and recreation areas 450Green land and graveyard. camping 450

Infrastructure facilityPlanned motorways 150Roads 100Railway lines 100Air ports 3000Power network 200Mine and dump areas 100

Cultural assetsCastle. cultural relict 1000

WetlandsStreams 250Inland water 200Flood area 200

Nature protectionNature reserves 500Projected nature reserves 500Landscape parks 200

licy 35 (2013) 257– 270

Distance [m]

Projected landscape parks 200Protected landscape areas 200Projected protected landscape areas 200Protective zone of landscape parks 200Nature 2000 500Areas of special protection of birds 1000Areas of special protection of habitats 500Ecological areas 500Documentation sites 500Nature monuments 100Landscape-nature complexes 200Ecological corridors 500Habitat of migrating birds 5000

Forest and semi natural areasForest 200Protected forest 500Orchards 50Forest of ecological significance 200Protected soil

Appendix 3. Site classification for wind turbines

Land use functions Constraints

Nature reserves HighProjected nature reserves HighAreas of special protection of birds (Natura 2000) HighAreas of special protection of habitats (Natura 2000) HighEcological corridors HighWater protection zones ModerateLandscape parks ModerateLandscape-nature complexes ModerateProtected landscape areas ModerateProjected landscape parks LowProjected protected landscape areas LowProtective zone of landscape parks LowProjected extension of landscape LowFloodplains High

Appendix 4. PV siting conditions for the slope andorientation

◦ Areas where the average slope is less than 3◦ (flat area) andregardless of orientation–condition 1 (the best condition).

◦ Areas where the average slope varies from 3◦ to 6◦ oriented to SE,S and SW (90◦–270◦)–condition 2 (good condition).

◦ Areas where the average slope varies between 6◦ and 15◦ andwhich are south oriented (135◦–225◦) – condition 3 (moderatecondition).

◦ Areas where the slope varies between 15◦ and 35◦ and whichare south oriented (157.5◦–202.5◦) – condition 4 (less moderatecondition).

◦ Others – condition 0 (unsuitable condition).

Appendix 5. Classification of land-use for PV siting

Classes Land-use based on CLC 2006

Favorable (F) Mines, dump sites, mineralextraction sites, bare rocks andburnt areasLand along railway lines andmotorway

Suitable-conflict (S-C) Arable land, pasturePossible-conflict (P-C) Fruit and beery plantationsUnsuitable (U) Urban areas, forestland,

wetland

Use Po

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R

A

A

A

B

B

B

B

B

C

D

D

D

D

E

E

B. Sliz-Szkliniarz / Land

ppendix 6. Site classification for ground mounted PVystems

Land use functions Constraints

Agricultural landSoil protected against non-agricultural use HighAgricultural land of moderate quality ModerateAgricultural land of low quality Low

Nature protectionNature reserves HighAreas of special protection of birds (Natura 2000) HighAreas of special protection of habitats (Natura 2000) HighEcological corridors HighWater protection zones ModerateLandscape parks ModerateLandscape-nature complexes ModerateProtected landscape areas ModerateProjected landscape parks LowProjected protected landscape areas LowProtective zone of landscape parks LowProjected extension of landscape Low

Natural hazardFloodplains High

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