Developing an integrated sustainability and resilience framework of indicators for the assessment of low-carbon energy technologies at the local level

Figures & data

Figure 1. Integrated MCA sustainability assessment framework for energy technologies.

Figure 2. The selection and validation process of indicators.

Table 1. Local case studies of sustainability assessment of energy technologies/options.

Local context	Study	Thematic area	Current vs. future energy options	Criteria selection	Real application
Chios Island, Greece	Haralambopoulos and Polatidis (2003)	Renewable energy projects	Current	By researchers	Proposed methodology
Sardinia Island, Italy	Beccali, Cellura, and Mistretta (2003)	Renewable energy technologies	Current	By researchers	Proposed methodology
Salina Island, Italy	Cavallaro and Ciraolo (2005)	Wind energy plants	Current	By researchers	Yes
Catalonia, Spain	Gamboa and Munda (2007)	Wind farm locations	Current	By researchers and stakeholders	Yes
Metropolitan Borough of Kirklees, UK	Burton and Hubacek (2007)	Small-scale energy technology applications	Current	By researchers	Yes
Local case study, Norway	Loken, Botterud, and Hoken (2009)	Future energy-supply infrastructure	Future	By researchers	Proposed methodology
Crete, Greece	Tsoutsos et al. (2009)	Sustainable energy planning	Current	By researchers	Yes
Urnasch, Switzerland	Trutnevyte, Stauffacher, and Scholz (2011)	Future energy systems	Future (2035)	By researchers and stakeholders	Yes
Thassos, Greece	Mourmouris and Potolias (2013)	Renewable energy sources	Current	By researchers	Proposed methodology
Crete, Greece	Kaldellis, Anestis, and Koronaki (2013)	Strategic electricity generation planning	Current	By researchers	Yes

Table 2. Final validated set of criteria and indicators.

Criteria categories	Indicators	Description
Environmental	CO2eq emissions	The indicator reflects the potential impacts of global climate change caused by greenhouse gas (GHG) emissions for the production of 1 kWh
	Climate resilience	The degree of resilience of the energy technology to the future climatic changes and extreme weather events
	Noise pollution	Part of population feeling highly affected by the noise caused due to the function of the energy facility. This indicator is case sensitive and could have been measured as a factor of the noise generation by the energy technology estimated in dB multiplied by the number of people affected by the noise. However since we are investigating different energy technologies and systems at a European scale we cannot measure precisely this indicator and therefore we will use an ordinal relevant scale to measure the perceived noise
	(Radioactive) waste	Amount of (radioactive) waste generated by the plant divided by energy produced
	Waste disposal (infrastructure)	Waste generation during the life cycle of the fuel and technology or availability of waste disposal infrastructure
	Ecosystem damages	This criterion quantifies the impacts to flora and fauna due to acidification and eutrophication caused by pollution from the production of 1 kWh electricity by the energy system and technology
	Land use requirement	The land required by each power plant and technology to be installed
	Fuel use	Amount of fuel use per kWh of final electricity consumption
Social	Level of public resistance/opposition	Energy system induced conflicts that may endanger the cohesion of society (e.g. nuclear, wind, CCS). Opposition might occur due to the perceptions of people regarding the catastrophic potential or other environmental impacts (aesthetic, odour, noise) of the energy technology/system. This indicator also integrates the aspect of participatory requirement for the application of the technology. The higher the public opposition, the higher the participatory requirement is
	Aesthetic/functional impact	Part of population that perceives a functional or aesthetic impairment of the landscape area caused by the energy system. The aesthetic impairment is judged subjectively and therefore this criterion fits in the social category than the environmental one. In addition this is also a very location specific indicator and therefore an average metric will be determined measured in relative ordinal scale
	Mortality and morbidity	Mortality and Morbidity due to air pollution caused by normal operation of the technology. This indicator is considered as an impact and composite indicator since it integrates all human health impacts caused from air pollutant emissions as NOx, SO2 and PM
	Accident fatalities	Lost of lifes of workers and public during installation and operation. Surrogate for risk aversion. This criterion partly integrates the catastrophic potential of the energy system/technology
Economic	Levelised costs (incl. capital, operational and maintenance, fuel costs)	Levelised costs of energy (LCOE): investment costs, operational and maintenance costs, capacity factor, efficiency, material use
	(Local) employment	The extent to which the application of the technology can create jobs at the investment, operation and maintenance stage. Furtheremore the criterion of employment reflects partly the extent of the impact that the technology has to the local economic development by providing jobs and generating income
Energy	Energy cost stability/sensitivity to fuel price fluctuation	The sensitivity of technology costs of electricity generation to energy and fuels prices fluctuations. The fraction of fuel cost to the overall electricity generation cost
	Stability of energy generation	Stability of output of electric power generated depending on the technology used. This reflects whether the energy supply is being interrupted. The presence of these interruptions impacts the electricity network stability. This criterion reflects whether the energy supply faces any interruptions due to the type of energy technology
	Peak load response	Technology specific ability to respond swiftly to large variation of demand in time / % representing the possibility to satisfy the required load
	Market concentration on supply	The market concentration on the supply of primary sources of energy that could lead to disruption due to economic or political reasons
Technological/Market	Technological maturity	The extent to which the technology is technically mature. The criterion refers to the level of technology's technological development and furthermore the spread of the technology at the market
	Market size (domestic)	Demand for final products (of energy technologies) and potential market size domestically. The potential market size plays an important role to establish industrial competitiveness and stimulate economic growth
	Market size (potential export)	Demand for final products (of energy technologies) and potential market size internationally
	Innovative ability	Flexibility and potential of the technology to integrate technological innovations

Figure 3. Survey results for environmental criteria.

Figure 4. Survey results for social criteria.

Figure 5. Survey results for economic criteria.

Figure 6. Survey results for energy criteria.

Figure 7. Survey results for technological criteria.

Figure 8. Validation survey overall results.

Appendix 1. The 20 most used assessment criteria for energy-related projects (Donkelaar ten and Amara 2010).

No.	Criterion	Category	Score
1	Energy and water use and savings	Environment	8
2	Raw materials use and savings	Environment	6
3	Greenhouse gas emissions	Environment	11
4	Air pollution	Environment	12
5	(Ground and surface) water pollution	Environment	10
6	Depletion of fresh water resources	Environment	8
7	Soil degradation	Environment	5
8	Waste creation and disposal	Environment	8
9	Use and management of hazardous chemicals and waste	Environment	5
10	Impact on biodiversity (or flora and fauna)	Environment	12
11	Impact on landscapes or land use	Environment	8
12	Noise	Environment	8
13	Cost efficiency	Economic	7
14	Employment creation	Economic	9
15	Health issues (mortality and morbidity)	Social	8
16	Safety issues (e.g. accident rates)	Social	5
17	Influence on food security	Social	6
18	Local income generation	Social	5
19	Education component /capacity building / awareness raising	Social	5
20	Equal opportunities	Social	5

Appendix 2. Final validated set of criteria and indicators and their literature sources.

Criteria categories	Indicators	Description	Sources
Environmental	CO2eq emissions	The indicator reflects the potential impacts of global climate change caused by emissions of GHGs for the production of 1 kWh	Afgan, Carvalho, and Hovanov (2000), Lee, Yoon, and Kim (2007), Diakoulaki and Karangelis (2007), Streimikiene et al. (2010), Burton and Hubacek (2007), Chatzimouratidis and Pilavachi (2007), Chatzimouratidis and Pilavachi (2008), Wang et al. (2009), Evans, Strezov, and Evans (2009), Jovanovic et al. (2009), Loken, Botterud, and Hoken (2009), Beccali, Cellura, and Mistretta (2003), Komor and Bazilian (2005), Shen, Li, and Yuan (2010), San Cristobal (2011)
	Climate resilience	The degree of resilience of the energy technology to the future climatic changes and extreme weather events	Ebinger and Vergara (2011), Dowling (2013)
	Noise pollution	Part of population feeling highly affected by the noise caused due to the function of the energy facility. This indicator is case sensitive and could have been measured as a factor of the noise generation by the energy technology estimated in dB multiplied by the number of people affected by the noise. However since we are investigating different energy technologies and systems at a European scale we cannot measure precisely this indicator and therefore we will use an ordinal relevant scale to measure the perceived noise	Evans, Strezov, and Evans (2009), Carrera and Mack (2010)
	(Radioactive) waste	Amount of (radioactive) waste generated by the plant divided by energy produced	NEEDS, Afgan, Carvalho, and Hovanov (2000), Streimikiene et al. (2010), Begic and Afgan (2007)
	Waste disposal (infrastructure)	Waste generation during the life cycle of the fuel and technology or availability of waste disposal infrastructure	Afgan, Carvalho, and Hovanov (2000), Streimikiene et al. (2010), Carrera and Mack (2010),
	Ecosystem damages	This criterion quantifies the impacts to flora and fauna due to acidification and eutrophication caused by pollution from the production of 1 kWh electricity by the energy system and technology	European Commission (2005), Evans, Strezov, and Evans (2009)
	Land use requirement	The land required by each power plant and technology to be installed	Afghan and Carvalho (2002), Beccali, Cellura, and Mistretta (2003), Streimikiene et al. (2010), Chatzimouratidis and Pilavachi (2008), Wang et al. (2009), Evans, Strezov, and Evans (2009), Shen, Li, and Yuan (2010)
	Fuel use	Amount of fuel use per kWh of final electricity consumption	Afgan, Carvalho, and Hovanov (2000)
Social	Level of public resistance/opposition	Energy system induced conflicts that may endanger the cohesion of society (e.g. nuclear, wind, CCS). Opposition might occur due to the perceptions of people regarding the catastrophic potential or other environmental impacts (aesthetic, odour, noise) of the energy technology/system. This indicator also integrates the aspect of participatory requirement for the application of the technology. The higher the public opposition, the higher the participatory requirement is	Tsoutsos et al. (2009), Carrera and Mack (2010),
	Aesthetic/functional impact	Part of population that perceives a functional or aesthetic impairment of the landscape area caused by the energy system. The aesthetic impairment is judged subjectively and therefore this criterion fits in the social category than the environmental one. In addition this is also a very location specific indicator and therefore an average metric will be determined measured in relative ordinal scale	Evans, Strezov, and Evans (2009), Carrera and Mack (2010)
	Mortality and Morbidity	Mortality and Morbidity due to air pollution caused by normal operation of the technology. This indicator is considered as an impact and composite indicator since it integrates all human health impacts caused from air pollutant emissions as NOx, SO2 and PM	Streimikiene et al. (2010), Carrera and Mack (2010), Afgan, Carvalho, and Hovanov (2000), Komor and Bazilian (2005), Diakoulaki and Karangelis (2007), Begic and Afgan (2007), Chatzimouratidis and Pilavachi (2009), Chatzimouratidis and Pilavachi (2008), Jovanovic et al. (2009), Shen, Li, and Yuan (2010)
	Accident fatalities	Lost of lifes of workers and public during installation and operation. Surrogate for risk aversion. This criterion partly integrates the catastrophic potential of the energy system/technology	Streimikiene et al. (2010), Chatzimouratidis and Pilavachi (2008), Carrera and Mack (2009), Afgan, Carvalho, and Hovanov (2000), Komor and Bazilian (2005), Diakoulaki and Karangelis (2007), Begic and Afgan (2007), Chatzimouratidis and Pilavachi (2009), Chatzimouratidis and Pilavachi (2008), Jovanovic et al. (2009), Shen, Li, and Yuan (2010)
Economic	Levelised costs (incl. capital, operational and maintenance, fuel costs)	Levelised costs of energy (LCOE): investment costs, operational and maintenance costs, capacity factor, efficiency, material use	Afgan, Carvalho, and Hovanov (2000), Mamlook, Akash, and Mohsen (2001), Afghan and Carvalho (2002), Liposcak et al. (2006), Diakoulaki and Karangelis (2007), Lee, Yoon, and Kim (2007), Madlener, Kowalski, and Stagl (2007), Begic and Afgan (2007), Doukas, H., B. Andreas, and J. Psarras (2007), Wang et al. (2009), Jovanovic et al. (2009), Chatzimouratidis and Pilavachi (2009), Tsoutsos et al. (2009), Shen, Li, and Yuan (2010), San Cristobal (2011)
	(Local) employment	The extent to which the application of the technology can create jobs at the investment, operation and maintenance stage. Furtheremore the criterion of employment reflects partly the extent of the impact that the technology has to the local economic development by providing jobs and generating income	Haralambopoulos and Polatidis (2003), Beccali, Cellura, and Mistretta (2003), Komor and Bazilian (2005), Erdoğmuş, Aras, and E. Koç (2006), Madlener, Kowalski, and Stagl (2007), Streimikiene et al. (2010), Doukas, H., B. Andreas, and J. Psarras (2007), Begic and Afgan (2007), Chatzimouratidis and Pilavachi (2007), Chatzimouratidis and Pilavachi (2008), Tsoutsos et al. (2009), Shen, Li, and Yuan (2010)
Energy	Energy cost stability/sensitivity to fuel price fluctuation	The sensitivity of technology costs of electricity generation to energy and fuels prices fluctuations. The fraction of fuel cost to the overall electricity generation cost	Mamlook, Akash, and Mohsen (2001), Komor and Bazilian (2005), Liposcak et al. (2006), Begic and Afgan (2007), Streimikiene et al. (2010), Wang et al. (2009), Jovanovic et al. (2009), Shen, Li, and Yuan (2010), Komor and Bazilian (2005), Burton and Hubacek (2007), Lund (2007), Cai et al. (2009), Shen, Li, and Yuan (2010)
	Stability of energy generation	Stability of output of electric power generated depending on the technology used. This reflects whether the energy supply is being interrupted. The presence of these interruptions impacts the electricity network stability. This criterion reflects whether the energy supply faces any interruptions due to the type of energy technology	Gross (2004), European Commission (2005), Taljan and Gubina (2009), Georgilakis and Katsigiannis (2009), Tsoutsos et al. (2009), Shen, Li, and Yuan (2010)
	Peak load response	Technology specific ability to respond swiftly to large variation of demand in time / % representing the possibility to satisfy the required load	Streimikiene et al. (2010), European Commission (2005)
	Market concentration on supply	The market concentration on the supply of primary sources of energy that could lead to disruption due to economic or political reasons	Carrera and Mack (2010)
Technological/market	Technological maturity	The extent to which the technology is technically mature. The criterion refers to the level of technology's technological development and furthermore the spread of the technology at the market	Beccali, Cellura, and Mistretta (2003), Huang, Chu, and Chiang (2008), Wang et al. (2009), Tsoutsos et al. (2009), Shen, Li, and Yuan (2010)
	Market size (domestic)	Demand for final products (of energy technologies) and potential market size domestically. The potential market size plays an important role to establish industrial competitiveness and stimulate economic growth	Lee, Yoon, and Kim (2007), Lewis and Wiser (2007), Lund (2009), Shen, Li, and Yuan (2010)
	Market size (potential export)	Demand for final products (of energy technologies) and potential market size internationally	Lee, Yoon, and Kim (2007), Lewis and Wiser (2007), Lund (2009), Shen, Li, and Yuan (2010)
	Innovative ability	Flexibility and potential of the technology to integrate technological innovations	Carrera and Mack (2010)

Haralambopoulos, D. A., and H. Polatidis. 2003. “Renewable Energy Projects: Structuring a Multi-Criteria Group Decision-making Framework.” Renewable Energy 28: 961–973. doi: 10.1016/S0960-1481(02)00072-1

 Web of Science ®Google Scholar

Beccali, M., M. Cellura, and M. Mistretta. 2003. “Decision Making in Energy Planning: The ELECTRE Method at Regional Level for the Diffusion of Renewable Energy Technology.” Renewable Energy 28: 2063–2087. doi: 10.1016/S0960-1481(03)00102-2

 Web of Science ®Google Scholar

Cavallaro, F., and L. Ciraolo. 2005. “A Multi-Criteria Approach to Evaluate Wind Energy Plants on an Italian Island.” Energy Policy 33: 235–244. doi: 10.1016/S0301-4215(03)00228-3

 Web of Science ®Google Scholar

Gamboa, G., and G. Munda. 2007. “The Problem of Windfarm Location: A Social Multi-Criteria Evaluation Framework.” Energy Policy 35: 1564–1583. doi: 10.1016/j.enpol.2006.04.021

 Web of Science ®Google Scholar

Burton, J., and K. Hubacek. 2007. “Is Small Beautiful? A Multicriteria Assessment of Smallscale Energy Technology Applications in Local Governments.” Energy Policy 35: 6402–6412. doi: 10.1016/j.enpol.2007.08.002

 Web of Science ®Google Scholar

Loken, E., A. Botterud, and A. Hoken. 2009. “Use of the Equivalent Attribute Technique in Multi-Criteria Planning of Local Energy Systems.” European Journal of Operational Research 197: 1075–1083. doi: 10.1016/j.ejor.2007.12.050

 Web of Science ®Google Scholar

Tsoutsos, T., M. Drandaki, N. Frantzeskaki, E. Iosifidis, and I. Kiosses. 2009. “Sustainability Energy Planning by Using Multi-Criteria Analysis Application in the Island of Crete, Greece.” Energy Policy 37: 1587–1600. doi: 10.1016/j.enpol.2008.12.011

 Web of Science ®Google Scholar

Trutnevyte, E., M. Stauffacher, and R. Scholz. 2011. “Supporting Energy Initiatives in Small Communities by Linking Visions with Energy Scenarios and Multi-Criteria Assessment.” Energy Policy 39: 7884–7895. doi: 10.1016/j.enpol.2011.09.038

 Web of Science ®Google Scholar

Mourmouris, J. C., and C. Potolias. 2013. “A Multi-Criteria Methodology for Energy Planning and Developing Energy Sources at A Regional Level: A Case Study Thassos, Greece.” Energy Policy 52: 522–350. doi: 10.1016/j.enpol.2012.09.074

 Web of Science ®Google Scholar

Kaldellis, J., A. Anestis, and I. Koronaki. 2013. “Strategic Planning in the Electricity Generation Sector Through the Development of an Integrated Delphi-Based Multi-Criteria Evaluation Model.” Fuel 106: 212–218. doi: 10.1016/j.fuel.2012.11.073

 Web of Science ®Google Scholar

Donkelaar ten, Michael, and Sliman Abu Amara. 2010. “Technical Input and Best Practices for Managing Authorities (and potential beneficiaries).” Work Package 2. Structural and Cohesion Funds for Sustainable Energy Investments.

 Google Scholar

Afgan, Naim H., Maria G. Carvalho, and Nikolai V. Hovanov. 2000. “Energy System Assessment with Sustainability Indicators.” Energy Policy 28 (9): 603–612. doi:10.1016/S0301-4215(00)00045-8.

 Web of Science ®Google Scholar

Lee, Seong Kon, Yong Jin Yoon, and Jong Wook Kim. 2007. “A Study on Making A Long-term Improvement in the National Energy Efficiency and GHG Control Plans by the AHP Approach.” Energy Policy 35 (5): 2862–2868. doi:10.1016/j.enpol.2006.09.019.

 Web of Science ®Google Scholar

Diakoulaki, D., and F. Karangelis. 2006. “Multi-criteria Decision Analysis and Cost–Benefit Analysis of Alternative Scenarios for the Power Generation Sector in Greece.” Renewable and Sustainable Energy Reviews 11 (4): 716–727. doi: 10.1016/j.rser.2005.06.007

 Web of Science ®Google Scholar

Streimikiene, D. 2010. “Comparative Assessment of Future Power Generation Technologies Based on Carbon Price Development.” Renewable and Sustainable Energy Reviews 14: 1283–1292. doi: 10.1016/j.rser.2009.12.001

 Web of Science ®Google Scholar

Chatzimouratidis, A. I., and P. A. Pilavachi. 2007. “Objective and Subjective Evaluation of Power Plants and their Non-radioactive Emissions Using the Analytic Hierarchy Process.” Energy Policy 35 (8): 4027–4038. doi: 10.1016/j.enpol.2007.02.003

 Web of Science ®Google Scholar

Chatzimouratidis, A., and P. Pilavachi. 2008. “Multicriteria Evaluation of Power Plants Impact on the Living Standard Using the Analytic Hierarchy Process.” Energy Policy 36 (3): 1074–1089. doi: 10.1016/j.enpol.2007.11.028

 Web of Science ®Google Scholar

Wang, J. J., Y. Y. Jing, C.F. Zhang, J.H. Zhao. 2009. “Review on Multi-criteria Decision Analysis Aid in Sustainable Energy Decision-Making.” Renewable and Sustainable Energy Reviews 13 (9): 2263–2278. doi: 10.1016/j.rser.2009.06.021

 Web of Science ®Google Scholar

Evans, A., V. Strezov, and T. J. Evans. 2009. “Assessment of Sustainability Indicators for Renewable Energy Technologies.” Renewable And Sustainable Energy Reviews, 13(5): 1082–1088. doi:10.1016/j.rser.2008.03.008.

 Web of Science ®Google Scholar

Jovanovic, M., N. Afgan, P. Radovanovic, and V. Stevanovic. 2009. “Sustainable Development of the Belgrade Energy System.” Energy 34 (5): 532–559. doi: 10.1016/j.energy.2008.01.013

 Web of Science ®Google Scholar

Komor, Paul, and Morgan Bazilian. 2005. “Renewable Energy Policy Goals, Programs, and Technologies.” Energy Policy 33 (14): 1873–1882. doi: 10.1016/j.enpol.2004.03.003

 Web of Science ®Google Scholar

Shen, G. Lin, K-P. Li, and B. Yuan. 2010. “An Assessment of Exploiting Renewable Energy Sources with Concerns of Policy and Technology.” Energy Policy 38 (8): 4604–4616. doi: 10.1016/j.enpol.2010.04.016

 Web of Science ®Google Scholar

San Cristobal, J.R. 2011. “Multi-criteria Decision-making in the Selection of a Renewable Energy Project in Spain: The Vikor Method.” Renewable Energy 36: 498–502. doi: 10.1016/j.renene.2010.07.031

 Web of Science ®Google Scholar

Ebinger, J., and W. Vergara. 2011. Climate Impacts on Energy Systems: Key Issues for Energy Sector Adaptation. Washington, DC: The World Bank.

 Google Scholar

Dowling, P. 2013. “The Impact of Climate Change on the European Energy System.” Energy Policy 60: 406–417. doi: 10.1016/j.enpol.2013.05.093

 Web of Science ®Google Scholar

Carrera, D., and A. Mack. 2010. “Sustainability Assessment of Energy Technologies via Social Indicators: Results of A Survey among European Energy Experts.” Energy Policy 38: 1030–1039. doi: 10.1016/j.enpol.2009.10.055

 Web of Science ®Google Scholar

Begic, F., and N. H. Afgan. 2007. “Sustainability Assessment Tool for the Decision Making in Selection of Energy System – Bosnian Case.” Energy 32: 1979–1985. doi: 10.1016/j.energy.2007.02.006

 Web of Science ®Google Scholar

European Commission. 2005. “ExternE: Externalities of Energy Methodology 2005 Update, Luxembourg.” http://www.externe.info/externe_d7/sites/default/files/methup05a.pdf.

 Google Scholar

Chatzimouratidis, A. I., P. A. Pilavachi. 2009. “Technological, Economic and Sustainability Evaluation of Power Plants using the Analytic Hierarchy Process.” Energy Policy 37 (3): 778–787. doi: 10.1016/j.enpol.2008.10.009

 Web of Science ®Google Scholar

Mamlook, R., B. A. Akash, M. S. Mohsen. 2001. “A Neuro-fuzzy Program Approach for Evaluating Electric Power Generation Systems. Energy 26 (6): 619–632. doi: 10.1016/S0360-5442(01)00015-9

 Web of Science ®Google Scholar

Afgan, N. H., and M. G. Carvalho. 2002. “Multi-criteria Assessment of New and Renewable Energy Power Plants.” Energy 27 (8): 739–755. doi: 10.1016/S0360-5442(02)00019-1

 Web of Science ®Google Scholar

Liposcak, M., N. H. Afgan, N. Duic, M. da Craca Carvalho. 2006. “Sustainability Assessment of Cogeneration Sector Development in Croatia.” Energy 31 (13): 2276–2284. doi: 10.1016/j.energy.2006.01.024

 Web of Science ®Google Scholar

Madlener, R., K. Kowalski, and S. Stagl. 2007. “New Ways for an Integrated Valuation of National Energy Scenarios: The Case of Renewable Energy use in Austria.” Energy Policy 35 (12): 6060–6074. doi: 10.1016/j.enpol.2007.08.015

 Web of Science ®Google Scholar

Doukas, H., B. Andreas, J. Psarras. 2007. “Multi-criteria Decision Aid for the Formulation of Sustainable Technological Energy Priorities using Linguistic Variables.” European Journal of Operational Research 182: 844–855. doi: 10.1016/j.ejor.2006.08.037

 Web of Science ®Google Scholar

Erdoğmuş, Ş., H. Aras, and E. Koç. 2006. “Evaluation of Alternative Fuels for Residential Heating in Turkey using Analytic Network Process (ANP) with Group Decision-making.” Renewable And Sustainable Energy Reviews, 10(3): 269–279. doi:10.1016/j.rser.2004.09.003.

 Web of Science ®Google Scholar

Lund, P.D. 2007. “The Link Between Political Decision-making and Energy options: Assessing Future Role of Renewable Energy and Energy Efficiency in Finland.” Energy 32 (12): 2271–2281. doi: 10.1016/j.energy.2007.05.015

 Web of Science ®Google Scholar

Cai, Y. P., G. H. Huang, Q. Tan, Z. F. Yang. 2009. “Planning of Community-scale Renewable Energy Management Systems in a Mixed Stochastic and Fuzzy Environment.” Renewable Energy 34 (7): 1833–1847. doi: 10.1016/j.renene.2008.11.024

 Web of Science ®Google Scholar

Gross, R. 2004. “Technologies and Innovation for System Change in the UK: Status, Prospects and System Requirements of Some Leading Renewable Energy Options.” Energy Policy 32 (17): 1905–1919. doi: 10.1016/j.enpol.2004.03.017

 Web of Science ®Google Scholar

Taljan, G., and A. F. Gubina. 2009. “Energy-based System Well-being Analysis for Small Systems with Intermittent Renewable Energy Sources.” Renewable Energy 34 (12): 2651–2661. doi: 10.1016/j.renene.2009.05.012

 Web of Science ®Google Scholar

Georgilakis, P.S., and Y. A. Katsigiannis. 2009. “Reliability and Economic Evaluation of Small Autonomous Power Systems Containing only Renewable energy sources.” Renewable Energy 34 (1), 65–70. doi: 10.1016/j.renene.2008.03.004

 Web of Science ®Google Scholar

Huang, C.C., P. Y. Chu and Y. H. Chiang. 2008. “A Fuzzy AHP Application in Governmentsponsored R&D Project Selection.” Omega 36 (6): 1038–1052. doi: 10.1016/j.omega.2006.05.003

 Web of Science ®Google Scholar

Lewis, J., and R. Wiser. 2005. Fostering A Renewable Energy Technology Industry: An International Comparison of Wind Industry Policy Support Mechanisms. Berkeley: LBNL.

 Google Scholar

Lund, P. D. 2009. “Effects of Energy Policies on Industry Expansion in Renewable Energy.” Renewable Energy 34: 53–64. doi: 10.1016/j.renene.2008.03.018

 Web of Science ®Google Scholar