Translator Disclaimer
1 May 2016 Using the “Footprint” Approach to Examine the Potentials and Impacts of Renewable Energy Sources in the European Alps
Author Affiliations +
Abstract

The expansion of renewable energies is regarded as a key way to mitigate global climate change and to ensure the provision of energy in the long term. However, conflicts between these goals and local nature conservation goals are likely to increase because of the additional space required for renewable energies. This is particularly true for mountainous areas with biodiversity-rich ecosystems. Little effort has been undertaken to systematically compare different renewable energy sources and to examine their environmental impacts using an interdisciplinary approach. This study adapted the concept of the “ecological footprint” to examine the impact on ecosystem services of land use changes involved in exploiting renewable energy sources. This innovative approach made it possible to assess and communicate the potentials of those energy sources in light of both space consumption and sustainability. The European Alps are an ideal test area because of their high energy potentials and biodiversity-rich ecosystems and the high demand for multiple ecosystem services. Our results demonstrate that energy consumption in the Alps could not be covered with the available renewable energy potentials, despite the utilization of large parts of the Alpine land area and the majority of larger rivers. Therefore, considerable effort must be invested in resolving conflicting priorities between expanding renewable energies and nature conservation, but also in realizing energy-saving measures. To this end, the approach presented here can support decision-making by revealing the energy potentials, space requirements, and environmental impacts of different renewable energy sources.

Introduction

The European Union defined the expansion of renewable energies (REs) as a key element in sustainable energy policy and the mitigation of global climate change and greenhouse gas emissions (European Commission 2009; IPCC 2011). Mountainous regions are strategically important because of their high energy potentials and ability to balance fluctuating energy production. However, on the local scale, the expansion of REs generates new conflicts between energy production and nature conservation goals (Jackson 2011). This is particularly true for mountainous areas because of their high biodiversity and increasing land use pressure (Sartoris et al 2012).

REs are not sustainable per se but require a trade-off between carbon-free energy production and local environmental, economic, and social interests (Evans et al 2009). Common tools to evaluate related impacts and conflicts include the Strategic Environmental Assessment and Environmental Impact Assessment (European Commission 2001). During the last decade, various decision-support systems and recommendations have been developed to analyze trade-offs between carbon-free energy generation and other interests. Most focus on a single energy source such as hydropower (eg Alemu et al 2010), forest biomass (eg Freppaz et al 2004; Frombo et al 2009) or wind energy (eg Voivontas et al 1998; Schillings et al 2012; Regio Energy 2015; Suisse Eole 2015). However, little effort has been made to compare the spatial requirements and impacts of different RE sources in a broader context prior to the planning of specific projects. Until now, this has only been realized in the context of regional energy plans (eg in eastern Germany; Peters et al 2006).

A systematic approach to support decision-making by comparing various RE sources’ energy potentials and environmental impacts has yet to be developed. Experience from the Alpine.Space project “recharge.green” underlines the need for new, interdisciplinary concepts to balance the expansion of RE with nature conservation goals (Svadlenak-Gomez et al 2013). Here we examine the potential of a footprint approach to reconcile RE expansion and ecosystem service maintenance with an emphasis on the special situation in mountainous areas. Based on the European Alpine area defined by the Secretariat of the Alpine Convention (2010), this approach is being tested and discussed, aiming to provide a starting point for discussions with decision-makers on conflicts and the spatial scales involved. Furthermore, the presented approach serves as a basis for developing a spatially explicit decision support tool in the Alpine.Space project “recharge.green.”

Analyzing RE space requirements is essential, as production rates strongly correlate with space, in contrast to fossil energy (Brücher 2009). Particularly in mountainous areas, the limited availability of space increasingly complicates the expansion of REs. The (human) ecological footprint approach, developed in the 1990s, was successful in communicating the concept of the limited resource “available space” on our planet and the scale of impacts related to human activities (Wackernagel et al 1993; Wackernagel and William 1997). A footprint approach can help to communicate and compare the land demand of different RE sources by calculating the area necessary to provide for the consumption of one person or a group of people. In this context, it is also possible to convey and compare the land requirements of different energy sources (Stöglehner 2003; Zhao et al 2005; Stöglehner and Narodoslawsky 2009).

However, the ecological footprint approach has also been criticized, in particular for not addressing environmental impacts and the sustainability of land use changes (Van den Bergh and Verbruggen 1999; Fiala 2008; Bergh and Grazi 2014). Therefore, we present an approach that incorporates not only spatial constraints, but also constraints related to ecosystem service impacts. In contrast to the ecological footprint approach that focuses on the space required to cover specific resource demands, we focus on energy outputs per defined space unit.

This step is necessary, as little attention has been paid until now to the sustainability of land use change in the context of expanding RE. Particularly in mountainous regions, a broad range of environmental, social, and economic factors constrains the potential of RE sources. The ecosystem services concept has great potential to accommodate this wide range of considerations (Sukhdev et al 2010). Defined as the benefits humans obtain from ecosystems (Millennium Ecosystem Assessment 2005), ecosystem services are often regarded as a way to measure nature in monetary terms (eg Paletto et al 2015). This approach also provides an interdisciplinary framework to assess the wide range of environmental, social, and economic impacts related to the expansion of RE (Hastik et al 2015) and to approximate a sustainable rate of RE exploitation expressed as “reduced” energy potentials.

In the next step we elucidate the relationship between land cover proportions and energy potentials. To do so, we downscale the total area of the European Alps (191,700 km2) to a smaller reference area of 1 km2. The linear downscaling of land cover proportions and energy potentials allows us to depict the spatial impact of different RE sources and to compare our results with those from other regions.

Methods

The RE “footprint”: a stepwise approach

We drew on the terminology of energy potentials provided by Resch et al (2008) to structure a stepwise procedure (Figure 1):

  1. Estimate the technical potential of each energy source within the 1-km2 reference area, taking into consideration theoretical physical energy potentials in the Alps and technical constraints such as conversion efficiency, as described in existing data sets and literature. These technical potentials serve as a basis to evaluate theoretical RE footprints.

  2. Define the constraints required to avoid generating negative ecological, social, and economic impacts while exploiting these potentials in the long run, to derive a “reduced potential” value.

  3. Correlate potentials and constraints with the land cover proportions of the research area.

  4. Reconcile potentials, comparing actual production and consumption based on the reference area and the respective population size.

FIGURE 1 

Determining the RE land use footprint.

i0276-4741-36-2-130-f01.tif

By comparing the outcomes of steps 3 and 4, decision-makers can capture the difference between actual RE production and demand, estimate land use and ecosystem service impacts if all RE sources are used, and use the information to help balance expansion of RE and other interests such as nature protection.

Test area and available data

Mountainous areas such as the Alps are particularly affected by expansion of RE because of their fragile ecosystems, high biodiversity, highly appreciated aesthetic and recreational values, and diversity of cultural identities (Hoppichler 2013). At the same time, the Alps are strategically important for the provision of energy for central Europe. The Alpine area defined by the Secretariat of the Alpine Convention (2010) (Figure 2) has a size of 191,700 km2, includes 8 countries with 14 million inhabitants, and is the holiday destination for approximately 120 million guests per year. Hydropower and forest biomass are traditional energy sources and represent the greatest RE contribution (Haberl et al 2001; Revaz 2001). Other RE sources such as solar energy, wind energy, biogas, and agricultural biomass are increasingly used (European Renewable Energy Council 2010).

FIGURE 2 

Land cover in the Alps. (Source: adapted from Hastik et al 2015)

i0276-4741-36-2-130-f02.tif

Currently, no literature systematically compares potentials and constraints for different energy sources in the Alps. Therefore, calculations were carried out based on available literature and geostatistical analyses. To do so, we used representative literature sources from individual regions or states, as few existing studies (Garegnani et al 2015) refer to the Alpine area as a whole. Because of the limited literature data available for wind and solar energy, we first calculated these energy potentials for the entire Alpine area (considering the spatial variability) and then downscaled these results for a mean 1-km2 reference area. Geostatistical calculations were carried out with GIS software (Arcgis, GRASS, QGis) based on available data sets for the entire area of the Alps (Corine land cover, 30 m digital elevation model). Energy production rates from 2009 to 2014 served as a basis to estimate current energy production rates (Supplemental material, Table S1:  http://dx.doi.org/10.1659/MRD-JOURNAL-D-15-00071.S1). In the face of existing data gaps for the Alpine area and related harmonization problems, energy consumption rates were based on statistics available for Austria (Statistik Austria 2014).

Results

Estimating average technical potentials per km2 for the Alps

Forest biomass potentials in the Alps vary strongly depending on soil fertility, climate, and tree species composition. A Swiss forestry inventory (Brändli 2010) indicated yearly stock increment values of 14.7 m3 ha−1 under optimal conditions (eg many northern Alpine foothills) and about 5 m3 ha−1 in less productive forests (many parts of the southern Alps). An adaption of the International Institute for Applied Systems Analysis global forest growth model (Kindermann et al 2006) indicates a mean annual value of 8.5 m3 ha−1 for the Alps. As weight and calorific content vary depending on tree species, we assumed a mean weight of 468.5 kg m−3 and a mean yearly stock increment of 4.5 kWh kg−1 based on Hahn (2007).

Hydropower generation generally depends on flow rates and topography. Nevertheless, statistical analyses have shown a correlation between directly impacted (dammed) river length and energy production rates (Schmutz et al 2010). Small hydropower facilities tend to have a larger impact on river courses, per unit of energy produced, than bigger facilities. (Pumped-storage hydropower is not addressed in this article, as its primary aim is to balance fluctuating energy production.)

Wind power potentials mainly depend on wind speed and energy conversion efficiency. Energy yields increase with hub height (higher wind speeds) and rotor diameter (larger harvest area). The footprint can be defined by the harvest area, which is measured in terms of the minimum distance between windmills and the diameter of the rotor (Peters et al 2006). Other footprint references, such as the soil sealed area or the visually impacted area, are not practicable for the Alpine-wide scale of this work. Based on a wind speed map for the Alps at a height of 70 m (Schaffner and Remund 2005) and various ecosystem-service-related constraints, an average annual wind yield (including at less favorable sites) of 2.2 GWh can be assumed.

Solar energy potentials depend mainly on geographical latitude, climate, topography, and conversion efficiency. For this study we focused on building-mounted photovoltaic units. Therefore, efficiency and performance ratio values were assumed according to current technological standards (European norm EN 15316-4-6). Urban areas in the Alps show a mean solar radiation of 1250 kWh m−2 or 150 GWh km−2 electricity output annually, according to geodata provided by the Photovoltaic Geographical Information System project (Šúri et al 2007; Huld et al 2012) and Corine land cover (CLC 2006).

Biogas power plants produce heat, electricity, and/or methane by the anaerobic digestion of manure, slurry, biological waste, and other types of biomass. Large quantities of maize-silage substrate are frequently used because of their high gas yields (Landesumweltanwaltschaften Österreichs 2013). Substrate based on grass silage is less productive in terms of energy output but abundant in many Alpine areas (Amon et al 2005, 2007; Prochnow et al 2009). Biogas energy based on slurry, manure, and organic waste was not analyzed in this study, because of the large uncertainties in the available data.

Comparing the resulting average technical potentials (Table 1) reveals high energy output for solar energy, followed by wind energy (Figure 3). Biomass-based energy sources show much lower values but are nevertheless important to balance energy demand fluctuations. Hydropower potentials per river length strongly depend on local terrain and project characteristics (Schmutz et al 2010); small hydropower plants tend to be less space efficient.

TABLE 1 

Calculation of the technical potential of RE sources in the Alps (annual values).

i0276-4741-36-2-130-t01.tif

FIGURE 3 

Mean annual RE output in the Alps.

i0276-4741-36-2-130-f03.tif

Defining ecosystem-service-related constraints that reduce potentials

Depending on the RE source, different ecosystem services impacts need to be considered when defining energy use constraints (Table 2). An intensive use of forest biomass might result in a deterioration of biodiversity, for instance due to reduced deadwood levels (Müller and Bütler 2010). Therefore, we assumed a reduced harvest of 70% in protected forest areas, as proposed by Hofer and Altwegg (2007). Besides habitat and biodiversity impacts, increased use of forest biomass for energy generation might result in resource competition with wood-processing industries (Rode et al 2005; Dahlquist and Bundenschuh 2013). As optimized cascaded use can help to reduce resource conflicts, we assumed that half of the industrial wood can be acquired for energy production (Österreichischer Biomasse-Verband 2013). Furthermore, natural hazard protection on steep slopes is regarded as a key function of many Alpine forests, requiring adapted forest management strategies. Data from Switzerland show that usage proportions in hazard protection forests vary between 20% and 51% (Hofer and Altwegg 2007). Finally, we assumed that most forest residues are left in place to ensure long-term soil fertility (Katzensteiner and Nemestothy 2007).

TABLE 2 

Ecosystem services impacts and assumed reductions in potential of selected RE sources.

i0276-4741-36-2-130-t02.tif

Hydroelectric power plants have an impact on aquatic and riverine ecosystems and are likely to hinder migration routes and decrease biological diversity (International Energy Agency 2000; Bunn and Arthington 2002). The recreational value of river courses can also be impacted by hydroelectric power plants. Based on the Water Frame Directive (European Commission 2000), we assumed that new hydropower plants cannot be installed in protected river areas and should not degrade existing river courses. We also assumed an avoidance of small hydropower because of its limited potential and substantial ecological impacts (Alpine Convention 2011).

Wind power projects need to consider possible impacts on endangered bird and bat species (Kunz et al 2007). Additionally, windmills alter the scenic beauty of landscapes, and this can influence tourism and people’s willingness to accept such projects (Jobert et al 2007). Therefore, we assumed a buffer area around protected areas and settlement areas (BFE et al 2004; Regio Energy 2015; Suisse Eole 2015). Additionally, wind energy is avoided in as yet-untouchedS alpine areas above 2500 m.

Ground-mounted solar energy production competes with the provision of agricultural products and might impact landscape aesthetics (Tsoutsos et al 2005). Therefore, we assumed that most Alpine regions promote building-mounted solar energy but strongly limit the use of ground-mounted solar units.

An Italian study recently questioned the compatibility of biogas facilities, which depend on intensive agriculture and a large amount of material inputs, with small-scale agriculture in the Alps (Magnani 2012). Therefore, we assumed that 10% of agricultural land could be used for energy crops, in contrast to 20% in some parts of Europe (Hellmann and Verburg 2011). This percentage can be further lowered because of the use of organic byproducts and waste materials.

Estimating reduced potentials on actual land cover

Once the technical potentials and related constraints are clarified, it is possible to calculate the footprint of all energy sources within a given spatial unit. Land cover proportions in the Alps based on the Corine land cover data (CLC 2006) were downscaled from 191,700 km2 to 1 km2. For rivers, we first calculated the river length for the entire Alps and then downscaled the river lengths for 1 km2. Within forest areas, hazard protection forests (19.5%; Bundesforschungszentrum für Wald 2011) and forests in protected areas (22%; authors’ GIS calculations) were distinguished. Furthermore, 5% of the forest area was considered natural forest, without economically motivated forestry activities (Hofer and Altwegg 2007), and 10% of the agricultural land was assumed to contribute to biogas production. The assumption regarding the proportion of buildings in urban areas used for solar energy (13.6%) was based on our own GIS analysis in Tyrol, Austria.

These proportions formed the basis for estimation of the RE footprint using all “reduced” potentials (Figure 4). Energy potentials mapped on land cover proportion, and hydropower potentials depicted by catchment area (Figure 4), show that nearly half of the Alpine land cover and a majority of larger river courses would be dedicated to energy production if all ecosystem-service-reduced potentials were used. In this scenario, forest biomass necessitates the highest land cover percentage (35%). Hydropower offers the highest energy potentials, whereas biomass can be regarded as an important flexible heat source. Solar energy potentials are substantial while impacting only a very small area. Biogas and wind energy could serve as supplementary energy sources. However, potential locations for wind energy projects are mostly limited to areas near and above the timberline.

FIGURE 4 

Annual RE potentials on a 1-km2 reference area representing land cover proportions in the Alps. Hydropower potentials are depicted by catchment area-classified river sizes. Mean river lengths are shown for 1 km2 according to catchment area: very small  =  <36 km2; small  =  36–360 km2; medium  =  360–3600 km2; large  = >3600 km2.

i0276-4741-36-2-130-f04.tif

Comparing potentials, production, and consumption

Results for the Alps (Table 3) and the 1-km2 reference area (Figure 5) indicate that about half of all RE potentials (based on the assumptions made in this study) are currently being used. Most hydropower potential is already realized, whereas substantial potential remains for solar and wind energy. Available forest biomass potentials already seem to be utilized to a large extent, if previously published recommendations (Thees et al 2013) on preserving ecosystem services are to be fulfilled. No data on the use of biogas could be found on an Alpine scale. However, this energy source can be regarded as minor compared to hydropower and forest biomass.

TABLE 3 

Annual RE potentials and current production in the total Alpine area (191,700 km2) and the 1-km2 reference area.

i0276-4741-36-2-130-t03.tif

FIGURE 5 

Annual RE potentials, actual production, and energy demand on a 1-km2 reference area. (Demand estimates based on consumption data from Statistik Austria 2014)

i0276-4741-36-2-130-f05.tif

Energy demand rates were based on energy consumption rates per capita in Austria and a mean population density of 73 inhabitants per km2 in the Alps. A comparison of energy potentials and demands shows that energy consumption exceeds RE potentials by 149%. Therefore, energy self-sufficiency can be reached only by implementing drastic energy-saving measures, even in the Alps.

Discussion and conclusion

Comparing energy potentials and current energy production reveals that solar energy has the highest potential for expansion, followed by wind energy and hydropower. In some regions, agricultural biomass could be used as a supplementary energy source. Much like other recent studies from the Alps (eg Hofer and Altwegg 2007; Thees et al 2013), our results suggest that most remaining forest biomass potentials result from underutilization during the last decades. However, this also indicates that any expanded use of biomass resources should be accompanied by energy-saving measures such as improved thermal insulation. In order for the Alps to become self-sufficient in RE, energy demands would need to be reduced and RE production would need to be raised. These goals would be even harder to fulfill if the Alps were to provide RE for central Europe and help supply an increasing demand by the transport sector for vehicles powered by electricity, biodiesel, or biogas (Hartmann and Özdemir 2011).

Land cover proportions depicted in the reference area show that nearly half of the Alpine land cover would be dedicated to energy production if all reduced energy potentials were used. The vast majority of this proportion is related to production of forest biomass, which is likely to cause few conflicts if managed sustainably. In contrast, wind energy is likely to cause conflicts, as most potentials can be found in natural high-Alpine areas as yet scarcely altered by human activities (Pröbstl et al 2011). Remaining hydropower potentials need to be reconciled carefully, as most of the bigger rivers are already intensively used and small hydropower projects tend to impact river ecosystems to a larger extent in relation to the amount of energy generated (Schmutz et al 2010; Platform Water Management in the Alps 2011). Comparing different RE sources revealed a trade-off between low-conflict but land-consuming and controversial but land use-efficient options. To increase the share of RE, a focus on sources with high energy but low conflict potential has been recommended (Sartoris et al 2012). However, particularly in the case of solar energy, problems related to energy storage and other costs need to be evaluated (Palzer and Henning 2014).

In contrast to the basic ecological footprint analysis, the approach described in this article incorporates ecosystem-service impacts and land use proportions. Therefore, this study makes an important contribution to discussion of the spatial extent and potential conflicts related to RE production. However, the sustainability of human actions such as expanding RE is only conclusively assessable on a local level and within a local context (Volken et al 2011). Because of the scaling involved in the study, and the heterogeneity of the Alps, this approach is not suited to identifying optimal energy production hotspots. As for the ecological footprint approach, problems related to defining system boundaries remain (Van den Bergh and Verbruggen 1999; Fiala 2008; Bergh and Grazi 2014). Nevertheless, these problems are inherent for RE, for instance in defining the size of energy-autonomous regions (Abegg 2011) and dealing with embodied energy that is transferred between regions via trade of goods.

The results of this study highlight the fact that balancing expanding RE and biodiversity conservation is of utmost importance in the Alps. Policy-makers can use this study as an important basis to define further scenarios with diverging assumptions in a GIS-based decision-support system, which is currently being developed in the Alpine.Space project “recharge.green.”

Open access article: please credit the authors and the full source.

ACKNOWLEDGMENTS

We thank the recharge.green pilot areas Vorarlberg, Triglav National Park, and Province of Belluno for their collaboration. This study is part of the Alpine.Space program. Special thanks to Dr P. K. Walzer for language editing. We would also like to thank the anonymous reviewers for their feedback and corrections.

REFERENCES

1.

B. Abegg 2011. Energy self-sufficient regions in the European Alps. Mountain Research and Development 31(4):367–371. Google Scholar

2.

ET Alemu RN Palmer A Polebitski B. Meaker 2010. Decision support system for optimizing reservoir operations using ensemble streamflow predictions. Journal of Water Resources Planning and Management 137(1):72–82. Google Scholar

3.

Alpine Convention, editor. 2011. Platform Water Management in the Alps - Situation Report on Hydropower Generation in the Alpine Focussing on Small Hydropower. Innsbruck, Austria, and Bolzano-Bozen, Italy: Permanent Secretariat of the Alpine Convention. Google Scholar

4.

T Amon B Amon V Kryvoruchko A Machmüller K Hopfner-Sixt V Bodiroza R Hrbek J Friedel E Pötsch H Wagentristl M Schreiner W. Zollitsch 2007. Methane production through anaerobic digestion of various energy crops grown in sustainable crop rotations. Bioresource Technology 98(17):3204–3212. Google Scholar

5.

T Amon V Kryvoruchko B Amon V Bodiroza W Zollitsch J Boxberger E. Pötsch 2005. Biogas production from grassland biomass in the Alpine region. Landtechnik 60(6):336–337. Google Scholar

6.

JC Bergh F. Grazi 2014. Ecological footprint policy? Land use as an environmental indicator. Journal of Industrial Ecology 18(1):10–19. Google Scholar

7.

BFE, BUWAL, ARE [Bundesamt für Energie, Bundesamt für Umwelt, Wald und Landschaft, Bundesamt für Raumentwicklung], editor. 2004. Konzept Windenergie Schweiz. Grundlagen für die Standortwahl von Windparks. Bern, Switzerland: ARGE Konzept Windenergie Schweiz.  http://www.juracretes.ch/d2wfiles/document/4970/5019/0/Schlussbericht-Windenergie-de.pdf; accessed on 24 March 2016. Google Scholar

8.

U-B Brändli editor. 2010. Schweizerisches Landesforstinventar. Ergebnisse der dritten Erhebung 2004–2006. Bern, Switzerland: Eidgenössische Forschungsanstalt für Wald, Schnee und Landschaft (WSL).. Google Scholar

9.

W. Brücher 2009. Energiegeographie: Wechselwirkungen zwischen Ressourcen, Raum und Politik. Stuttgart, Germany: Borntraeger. Google Scholar

10.

Bundesforschungszentrum für Wald. 2011. Österreichische Waldinventur 2007/09.  http://bfw.ac.at; accessed on 30 March 2015. Google Scholar

11.

SE Bunn AH. Arthington 2002. Basic principles and ecological consequences of altered flow regimes for aquatic biodiversity. Environmental Management 30(4):492–507. Google Scholar

12.

CLC [Corine Land Cover]. 2006. Corine Land Cover 2006 Seamless Vector Data.  http://www.eea.europa.eu/data-and-maps/data/clc-2006-vector-data-version; accessed on 30 March 2015. Google Scholar

13.

E Dahlquist J. Bundenschuh 2013. Pulp and paper industry—Trends for the future. In: E Dahlquist editor. Biomass as Energy Source: Resources, Systems and Applications. Boca Raton, FL: CRC Press, pp 229–254. Google Scholar

14.

European Commission. 2000. Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for community action in the field of water policy. Official Journal of the European Union L327(22.12.2000): 1–73.  http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32000L0060; accessed on 24 March 2016 . Google Scholar

15.

European Commission. 2001. Directive 2001/42/EC of the European Parliament and of the Council of 27 June 2001 on the assessment of the effects of certain plans and programmes on the environment. Official Journal of the European Union L197(21.7.2001): 30–37.  http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32001L0042; accessed on 24 March 2016. Google Scholar

16.

European Commission. 2009. Directive 2009/28/EC of the European Parliament and of the Council of 23 April 2009 on the promotion of the use of energy from renewable sources and amending and subsequently repealing Directives 2001/77/EC and 2003/30. Official Journal of the European Union L140(5.6.2009): 16–62.  http://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32009L0028; accessed on 24 March 2016. Google Scholar

17.

European Renewable Energy Council. 2010. Renewable Energy in Europe: Markets, Trends, and Technologies. London, United Kingdom, and Washington, DC: Earthscan. Google Scholar

18.

A Evans V Strezov TJ. Evans 2009. Assessment of sustainability indicators for renewable energy technologies. Renewable and Sustainable Energy Reviews 13(5):1082–1088. Google Scholar

19.

Fachagentur Nachwachsende Rohstoffe, editor. 2013. Basisdaten Bioenergie Deutschland. Gülzow-Prüzen, Germany: Fachagentur Nachwachsende Rohstoffe. Google Scholar

20.

N. Fiala 2008. Measuring sustainability: Why the ecological footprint is bad economics and bad environmental science. Ecological Economics 67(4):519–525. Google Scholar

21.

D Freppaz R Minciardi M Robba M Rovatti R Sacile A. Taramasso 2004. Optimizing forest biomass exploitation for energy supply at a regional level. Biomass and Bioenergy 26(1):15–25. Google Scholar

22.

F Frombo R Minciardi M Robba R. Sacile 2009. A decision support system for planning biomass-based energy production. Energy 34(3):362–369. Google Scholar

23.

G Garegnani P Zambelli G Grilli D. Vettorato Evaluation of wind, solar and hydro energy potential using GRASS. FOSS4G-Europe Conference, July 14th–17th 2015, Como, Italy.  http://geomatica.como.polimi.it/workbooks/n12/FOSS4G-eu15_submission_81.pdfaccessed on 31 March 2016. Google Scholar

24.

H Haberl H Adensam V. Kloud 2001. Alpenreport: Daten, Fakten, Probleme, Lösungsansätze. Schaan, Switzerland: CIPRA. Google Scholar

25.

J. Hahn 2007. Umrechnungszahlen und Verkaufsmaße von Scheitholz. LWF aktuell 61:24–25. Google Scholar

26.

N Hartmann ED. Özdemir 2011. Impact of different utilization scenarios of electric vehicles on the German grid in 2030. Journal of Power Sources 196(4):2311–2318. Google Scholar

27.

R Hastik S Basso C Geitner C Haida A Poljanec A Portaccio B Vrščaj C. Walzer 2015. Renewable energies and ecosystem service impacts. Renewable and Sustainable Energy Reviews 48:608–623. Google Scholar

28.

F Hellmann PH. Verburg 2011. Spatially explicit modelling of biofuel crops in Europe. Biomass and Bioenergy 35(6):2411–2424. Google Scholar

29.

P Hofer J. Altwegg 2007. Holz-Nutzungspotenziale im Schweizer Wald auf Basis LFI3. Bericht erstellt im Auftrag des Bundesamtes für Umwelt (BAFU). Bern, Switzerland: BAFU. Google Scholar

30.

J. Hoppichler 2013. Vom Wert der Biodiversität. Wirtschaftliche Bewertungen und Konzepte für das Berggebiet. Forschungsbericht 67. Vienna, Austria: Bundesanstalt für Bergbauernfragen. Google Scholar

31.

T Huld R Müller A. Gambardella 2012. A new solar radiation database for estimating PV performance in Europe and Africa. Solar Energy 86(6):1803–1815. Google Scholar

32.

International Energy Agency. 2000. Implementing Agreement for Hydropower Technologies and Programmes Annex III: Hydropower and the Environment. Paris, France: International Energy Agency. Google Scholar

33.

IPCC [Intergovernmental Panel on Climate Change]. 2011. Special Report on Renewable Energy Sources and Climate Change Mitigation. Prepared by Working Group III of the IPCC: Edenhofer O, Pichs-Madruga R, Sokona Y, Seyboth K, Matschoss P, Kadner S, Zwickel T, Eickemeier P, Hansen G, Schlömer S, von Stechow C, editors. Cambridge, United Kingdom, and New York, NY: Cambridge University Press. . Google Scholar

34.

ALR. Jackson 2011. Renewable energy vs. biodiversity: Policy conflicts and the future of nature conservation. Global Environmental Change 21(4):1195–1208. Google Scholar

35.

A Jobert P Laborgne S. Mimler 2007. Local acceptance of wind energy: Factors of success identified in French and German case studies. Energy Policy 35(5):2751–2760. Google Scholar

36.

K Katzensteiner KP. Nemestothy 2007. Energetische Nutzung von Biomasse aus dem Wald und Bodenschutz – ein Widerspruch? Mitteilungen der Österreichischen Bodenkundlichen Gesellschaft 74:5–15. Google Scholar

37.

GE Kindermann M Obersteiner E Rametsteiner I. McCallum 2006. Predicting the deforestation-trend under different carbon-prices. Carbon Balance and Management 1(1):15. Google Scholar

38.

TH Kunz EB Arnett BM Cooper WP Erickson RP Larkin T Mabee ML Morrison M Strickland JM. Szewczak 2007. Assessing impacts of wind‐energy development on nocturnally active birds and bats: a guidance document. Journal of Wildlife Management 71(8):2449–2486. Google Scholar

39.

Landesumweltanwaltschaften Österreichs, editor. 2013. Nachhaltige Nutzung von Bioenergie in Österreich. Faktenlage und Forderungen der Landesumweltanwaltschaften. Vienna, Austria: Publisher. . Google Scholar

40.

N. Magnani 2012. Exploring the local sustainability of a green economy in alpine communities: A case study of a conflict over a biogas plant. Mountain Research and Development 32(2):109–116. Google Scholar

41.

Millennium Ecosystem Assessment. 2005. Ecosystems and Human Well-being. Washington, DC: Island Press. Google Scholar

42.

J Müller R. Bütler 2010. A review of habitat thresholds for dead wood: a baseline for management recommendations in European forests. European Journal of Forest Research 129(6):981–992. Google Scholar

43.

Österreichischer Biomasse-Verband, editor. 2013. Basisdaten 2013 Bioenergie. Vienna, Austria: Austrian Energy Agency. Google Scholar

44.

A Paletto C Geitner G Grilli R Hastik F Pastorella L. Rodrìguez Garcìa 2015. Mapping the value of ecosystem services: A case study from the Austrian Alps. Annals of Forest Research 58(1).  http://dx.doi.org/10.15287/afr.2015.335Google Scholar

45.

A Palzer H-M. Henning 2014. A comprehensive model for the German electricity and heat sector in a future energy system with a dominant contribution from renewable energy technologies, Part II: Results. Renewable and Sustainable Energy Reviews 30:1019–1034.  http://dx.doi.org/10.1016/j.rser.2013.11.032Google Scholar

46.

J Peters D Günnewig U Graumann J Naumann R Pohl M. Reichmuth 2006. Flächenbedarfe und kulturland-schaftliche Auswirkungen regenerativer Energien am Beispiel der Region Uckermark-Barnim. Hannover, Eberswalde, Leipzig, and Würzburg, Germany: Bundesamt für Bauwesen und Raumordnung. Google Scholar

47.

Platform Water Management in the Alps. 2011. Common Guidelines for the Use of Small Hydropower in the Alpine Region. Innsbruck, Austria, and Bozen, Italy: Permanent Secretariat of the Alpine Convention. Google Scholar

48.

U Pröbstl A Jiricka F. Hindinger 2011. Renewable energy in winter sport destinations - Desired, ignored or rejected? In: A Borsdorf J Stötter E Veulliet editors. Managing Alpine Future II. Proceedings of the Innsbruck Conference. IGF-Forschungsberichte 4. Vienna, Austria: Verlag der Österreichischen Akademie der Wissenschaften, pp 309–318. Google Scholar

49.

A Prochnow M Heiermann M Plöchl B Linke C Idler T Amon PJ. Hobbs 2009. Bioenergy from permanent grassland: A review: 1. Biogas. Bioresource Technology 100(21):4931–4944. Google Scholar

50.

Regio Energy. 2015. Windkraft - Vorläufige Ergebnisse Technisches Potenzial.  http://regioenergy.oir.at/windenergie/technisches-potenzial; accessed on 30 March 2015. Google Scholar

51.

G Resch A Held T Faber C Panzer F Toro R. Haas 2008. Potentials and prospects for renewable energies at global scale. Energy Policy 36(11):4048–4056. Google Scholar

52.

M. Revaz 2001. Holzenergie: Die Wärme aus dem Wald. In: Internationale Alpenschutzkommission (CIPRA), editor. 2. Alpenreport: Daten, Fakten, Probleme, Lösungsansätze. Bern, Switzerland: Paul Haupt, pp 307–309. Google Scholar

53.

M Rode C Schneider G Ketelhake D. Reißhauer 2005. Naturschutzverträgliche Erzeugung und Nutzung von Biomasse zur Wärme- und Stromgewinnung. BfN-Skripten 136. Bonn, Germany: BfN. Google Scholar

54.

A Sartoris J Fuhrer B Abegg E. Reynard 2012. Lösungsansätze für die Schweiz im Konfliktfeld erneuerbare Energien und Raumnutzung. Bern, Switzerland: Swiss Academies of Arts and Sciences. Google Scholar

55.

B Schaffner J. Remund 2005. The Alpine Space Wind Map: Modeling Approach. Alpine Windharvest Report Series 7-2. Bern, Switzerland: Meteotest. Google Scholar

56.

C Schillings T Wanderer L Cameron JT van der Wal J Jacquemin K. Veum 2012. A decision support system for assessing offshore wind energy potential in the North Sea. Energy Policy 49:541–551. Google Scholar

57.

S Schmutz R Schinegger S Muhar S Preis M. Jungwirth 2010. Ökologischer Zustand der Fließgewässer Österreichs–Perspektiven bei unterschiedlichen Nutzungsszenarien der Wasserkraft. Österreichische Wasser-und Abfallwirtschaft 62(7–8):162–167. Google Scholar

58.

Secretariat of the Alpine Convention. 2010. The Alps—People and Pressures in the Mountains: The Facts at a Glance. Bolzano-Bozen, Italy: Publisher. Google Scholar

59.

Statistik Austria. 2014. Gesamtenergiebilanz Österreich 1970 bis 2013.  http://www.statistik.at/web_de/statistiken/energie_und_umwelt/energie/energiebilanzen/index.html; accessed on 30 March 2015. Google Scholar

60.

G. Stöglehner 2003. Ecological footprint—a tool for assessing sustainable energy supplies. Journal of Cleaner Production 11(3):267–277. Google Scholar

61.

G Stöglehner M. Narodoslawsky 2009. How sustainable are biofuels? Answers and further questions arising from an ecological footprint perspective. Bioresource Technology 100(16):3825–3830. Google Scholar

62.

Suisse Eole. 2015. Konzept Windenergie Schweiz.  http://wind-data.ch/windkarte/potenzial.php; accessed on 30 March 2015. Google Scholar

63.

P Sukhdev H Wittmer C Schröter-Schlaack C Nesshöver J Bishop P ten Brink H Gundimeda P Kumar B. Simmons 2010. The Economics of Ecosystems and Biodiversity: Mainstreaming the Economics of Nature: A Synthesis of the Approach, Conclusions and Recommendations of TEEB. Geneva, Switzerland: The Economics of Ecosystems and Biodiversity (TEEB) and United Nations Environment Programme. Google Scholar

64.

M Šúri TA Huld ED Dunlop HA. Ossenbrink 2007. Potential of solar electricity generation in the European Union member states and candidate countries. Solar Energy 81(10):1295–1305. Google Scholar

65.

K Svadlenak-Gomez M Badura F Kraxner S Fuss D Vettorato C. Walzer 2013. Valuing Alpine ecosystems: The recharge.green project will help decision-makers to reconcile renewable energy production and biodiversity conservation in the Alps. eco.mont-Journal on Protected Mountain Areas Research 5:59–62. Google Scholar

66.

O Thees E Kaufmann R Lemm A. Bürgi 2013. Energieholzpotenziale im Schweizer Wald. Schweizeizerische Zeitchrift für Forstwesen 164(12):351–364. Google Scholar

67.

T Tsoutsos N Frantzeskaki V. Gekas 2005. Environmental impacts from the solar energy technologies. Energy Policy 33(3):289–296. Google Scholar

68.

JC Van den Bergh H. Verbruggen 1999. Spatial sustainability, trade and indicators: an evaluation of the “ecological footprint.”. Ecological Economics 29(1):61–72. Google Scholar

69.

D Voivontas D Assimacopoulos A Mourelatos J. Corominas 1998. Evaluation of renewable energy potential using a GIS decision support system. Renewable Energy 13(3):333–344. Google Scholar

70.

E Volken T Scheurer G Plassmann A. Wallner 2011. Energy production from renewable sources in Alpine protected areas; Conflicting interests and need for action as seen by protected areas management. eco.mont—Journal on Protected Mountain Areas Research 3:59–62. Google Scholar

71.

M Wackernagel J McIntosh WE Rees R. Woollard 1993. How Big Is Our Ecological Footprint. A Handbook for Estimating a Community’s Appropriated Carrying Capacity. Discussion draft of the Task Force on Planning Healthy and Sustainable Communities, University of British Columbia. Vancouver, BC, Canada: University of British Columbia. Google Scholar

72.

MR Wackernagel E. William 1997. Our Ecological Footprint: Reducing Human Impact on the Earth. Gabriola Island, BC, Canada; New Society. Google Scholar

73.

S Zhao Z Li W. Li 2005. A modified method of ecological footprint calculation and its application. Ecological Modelling 185(1):65–75. Google Scholar

Supplemental material

TABLE S1 References on energy production in the European Alps.

Found at DOI:  http://dx.doi.org/10.1659/MRD-JOURNAL-D-15-00071.S1 (92KB PDF).

© 2016 Hastik et al.
Richard Hastik, Chris Walzer, Christin Haida, Giulia Garegnani, Simon Pezzutto, Bruno Abegg, and Clemens Geitner "Using the “Footprint” Approach to Examine the Potentials and Impacts of Renewable Energy Sources in the European Alps," Mountain Research and Development 36(2), 130-140, (1 May 2016). https://doi.org/10.1659/MRD-JOURNAL-D-15-00071.1
Received: 1 January 2016; Accepted: 1 March 2016; Published: 1 May 2016
JOURNAL ARTICLE
11 PAGES


SHARE
ARTICLE IMPACT
Back to Top