Skip to main content
SpringerLink
Log in

Technology Assessment: Developing Geothermal Energy Resources for Supporting Electrical System in Oregon

  • Chapter
  • First Online:
Infrastructure and Technology Management

Part of the book series: Innovation, Technology, and Knowledge Management ((ITKM))

Abstract

This chapter presents a review of multi criteria decision models used in the energy sector and demonstrates application through the case of geothermal energy. The case is taken from Oregon which is located in teh pacific northwest region of the US. Experts are used to determine the criteria what is important for this application and the region.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Beck, F., & Martinot, E. (2004). Renewable energy policies and barriers. Encyclopedia of Energy, 5(7), 365–383.

    Article  Google Scholar 

  2. Zhe, W., Yiru, W., Chuan, H., Jianhui, Y., & Hao, Z. (2009). Development status of China's renewable energy power generation. InSustainable Power Generation and Supply, 2009. SUPERGEN'09. International Conference (pp. 1–5).

    Google Scholar 

  3. Wiser, R. H., & Pickle, S. J. (1998). Financing investments in renewable energy : the impacts of policy design. Renewable and Sustainable Energy Reviews, 2(4), 361–386.

    Article  Google Scholar 

  4. DiPippo, R. (1991). Geothermal energy electricity generation and environmental impact. Energy Policy, 19(8), 798–807.

    Article  Google Scholar 

  5. Sawyer, S. W. (1982). Leaders in change: Solar energy owners and the implications for future adoption rates. Technological Forecasting and Social Change, 21(3), 201–211.

    Article  Google Scholar 

  6. Energy.gov, Geothermal | Department of Energy. (2015). [Online]. Available: http://www.energy.gov/science-innovation/energy-sources/renewable-energy/geothermal. Accessed 25 Jun 2015.

  7. Barbier, E. (2002). Geothermal energy technology and current status: an overview. Renewable and Sustainable Energy Reviews, 6(1), 3–65.

    Article  Google Scholar 

  8. Oregon.gov, Geothermal Energy Geothermal Energy. (2015). [Online]. Available: http://www.oregon.gov/energy/renew/geothermal/pages/geo_index.aspx. Accessed 25 Jun 2015.

  9. Union of Concerned Scientists, 'How Geothermal Energy Works. (2015). [Online]. Available: http://www.ucsusa.org/clean_energy/our-energy-choices/renewable-energy/how-geothermal-energy-works.html#.VYukZ_lVikp. Accessed 25 Jun 2015.

  10. Lsa.colorado.edu, Geothermal Energy, 2015. [Online]. Available: http://lsa.colorado.edu/essence/texts/geothermal.html. Accessed 25 Jun 2015.

  11. Bertani, R. (2005). World geothermal power generation in the period 2001–2005. Geothermics, 34(6), 651–690.

    Article  Google Scholar 

  12. Bertani, R. (2012). Geothermal power generation in the world 2005–2010 update report. Geothermics, 41, 1–29.

    Article  Google Scholar 

  13. 2013 Annual US geothermal power production and development report, Geothermal Energy Association, Washington, D.C, 2013.

    Google Scholar 

  14. Lund, J. W. (January 2003). Direct-use of geothermal energy in the USA. Applied Energy, 74(1), 33–42.

    Article  Google Scholar 

  15. Lund, J. W. (1997). Geothermal research at the Geo-Heat Center Oregon Institute of Technology. InEnergy Conversion Engineering Conference, 1997. IECEC-97., Proceedings of the 32nd Intersociety (Vol. 3, pp. 1820–1825).

    Google Scholar 

  16. Lund, J. W., & Freeston, D. H. (2001). World-wide direct uses of geothermal energy 2000. Geothermics, 30(1), 29–68.

    Article  Google Scholar 

  17. Eia.gov. (2015). [Online]. Available: http://www.eia.gov/state/analysis.cfm?sid=OR. Accessed 25 Jun 2015.

  18. U.S.Department of Energy, EERE Investments in Oregon, 2013.

    Google Scholar 

  19. Petty, S., Nordin, Y., Glassley, W., Cladouhos, T. T., & Swyer, M. (2013). Improving geothermal project economics with multi-zone stimulation: results from the Newberry Volcano EGS demonstration. InProceedings of the 38th Workshop on Geothermal Reservoir Engineering (pp. 11–13). Stanford.

    Google Scholar 

  20. Hull, D. A., Bowen, R. G., Blackwell, D. D., & Peterson, N. V. (1977). Preliminary Heat-Flow Map and Evaluation of Oregon's Geothermal Energy Potential. The Ore Bin, 39(7), 109–123.

    Google Scholar 

  21. Pohekar, S. D., & Ramachandran, M. (2004). Application of multi-criteria decision making to sustainable energy planning—a review. Renewable and Sustainable Energy Reviews, 8(4), 365–381.

    Article  Google Scholar 

  22. Wang, J. J., Jing, Y. Y., Zhang, C. F., & Zhao, J. H. (2009). Review on multi-criteria decision analysis aid in sustainable energy decision-making. Renewable and Sustainable Energy Reviews, 13(9), 2263–2278.

    Article  Google Scholar 

  23. Løken, E. (2007). Use of multicriteria decision analysis methods for energy planning problems. Renewable and Sustainable Energy Reviews, 11(7), 1584–1595.

    Article  Google Scholar 

  24. Mendoza, G. A., & Martins, H. (2006). Multi-criteria decision analysis in natural resource management: a critical review of methods and new modelling paradigms. Forest Ecology and Management, 230(1), 1–22.

    Article  Google Scholar 

  25. Energy Challenges for Oregon and the Nation, National Ocean Industrial Association.

    Google Scholar 

  26. Adams, S., & Anderson, S. (2009). ‘'Portland Plan’'. InCity of Portland Bureau of Planning and Sustainability, Portland.

    Google Scholar 

  27. State of Oregon Biennial Energy Plan 2015–2017, Oregon Department of Energy, Salem, Oregon, 2015.

    Google Scholar 

  28. Samouilidis, J. E., & Mitropoulos, C. S. (1982). An aggregate model for energy costs: national product interdependence. Energy Economics, 4(3), 199–206.

    Article  Google Scholar 

  29. Bopp, A., & Lady, G. M. (1982). On measuring the effects of higher energy prices. Energy Economics, 4(4), 218–224.

    Article  Google Scholar 

  30. Meier, P., & Mubayi, V. (1983). Modelling energy-economic interactions in developing countries: A linear programming approach. European Journal of Operational Research, 13(1), 41–59.

    Article  Google Scholar 

  31. Beccali, M., Cellura, M., & Mistretta, M. (2003). Decision-making in energy planning. Application of the Electre method at regional level for the diffusion of renewable energy technology. Renewable Energy, 28(13), 2063–2087.

    Article  Google Scholar 

  32. San Cristóbal, J. R. (2011). Multi-criteria decision-making in the selection of a renewable energy project in Spain: the Vikor method. Renewable Energy, 36(2), 498–502.

    Article  Google Scholar 

  33. Kaya, T., & Kahraman, C. (2011). Multicriteria decision making in energy planning using a modified fuzzy TOPSIS methodology. Expert Systems with Applications, 38(6), 6577–6585.

    Article  Google Scholar 

  34. Beccali, M., Cellura, M., & Ardente, D. (1998). Decision making in energy planning: the ELECTRE multicriteria analysis approach compared to a fuzzy-sets methodology. Energy Conversion and Management, 39(16), 1869–1881.

    Article  Google Scholar 

  35. Zhou, P., Ang, B. W., & Poh, K. L. (2006). Decision analysis in energy and environmental modeling: An update. Energy, 31(14), 2604–2622.

    Article  Google Scholar 

  36. Lind, R. C. (1995). Intergenerational equity, discounting, and the role of cost-benefit analysis in evaluating global climate policy. Energy Policy, 23(4), 379–389.

    Article  Google Scholar 

  37. Maddison, D. (1995). A cost-benefit analysis of slowing climate change. Energy Policy, 23(4), 337–346.

    Article  Google Scholar 

  38. Diakoulaki, D., & Karangelis, F. (2007). 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.

    Article  Google Scholar 

  39. Snyder, B., & Kaiser, M. J. (2009). Ecological and economic cost-benefit analysis of offshore wind energy. Renewable Energy, 34(6), 1567–1578.

    Article  Google Scholar 

  40. Jaffe, A. B., Newell, R. G., & Stavins, R. N. (2005). A tale of two market failures: Technology and environmental policy. Ecological Economics, 54(2), 164–174.

    Article  Google Scholar 

  41. Clinch, J. P., & Healy, J. D. (2001). Cost-benefit analysis of domestic energy efficiency. Energy Policy, 29(2), 113–124.

    Article  Google Scholar 

  42. Bollen, J., van der Zwaan, B., Brink, C., & Eerens, H. (2009). Local air pollution and global climate change: A combined cost-benefit analysis. Resource and Energy Economics, 31(3), 161–181.

    Article  Google Scholar 

  43. Molinos-Senante, M., Hernández-Sancho, F., & Sala-Garrido, R. (2011). Cost–benefit analysis of water-reuse projects for environmental purposes: a case study for Spanish wastewater treatment plants. Journal of Environmental Management, 92(12), 3091–3097.

    Article  Google Scholar 

  44. Mehta, S., & Shahpar, C. (2004). The health benefits of interventions to reduce indoor air pollution from solid fuel use: a cost-effectiveness analysis. Energy for Sustainable Development, 8(3), 53–59.

    Article  Google Scholar 

  45. Kovacevic, V., & Wesseler, J. (2010). Cost-effectiveness analysis of algae energy production in the EU. Energy Policy, 38(10), 5749–5757.

    Article  Google Scholar 

  46. Bassi, A. M., & Yudken, J. S. (2011). Climate policy and energy-intensive manufacturing: A comprehensive analysis of the effectiveness of cost mitigation provisions in the American Energy and Security Act of 2009. Energy Policy, 39(9), 4920–4931.

    Article  Google Scholar 

  47. Jackson, T. (1995). Joint implementation and cost-effectiveness under the Framework Convention on Climate Change. Energy Policy, 23(2), 117–138.

    Article  Google Scholar 

  48. Berndes, G., & Hansson, J. (2007). Bioenergy expansion in the EU: cost-effective climate change mitigation, employment creation and reduced dependency on imported fuels. Energy Policy, 35(12), 5965–5979.

    Article  Google Scholar 

  49. Zomer, R. J., Trabucco, A., Bossio, D. A., & Verchot, L. V. (2008). Climate change mitigation: A spatial analysis of global land suitability for clean development mechanism afforestation and reforestation. Agriculture, Ecosystems & Environment, 126(1), 67–80.

    Article  Google Scholar 

  50. Nelson, D. B., Nehrir, M. H., & Wang, C. (2006). Unit sizing and cost analysis of stand-alone hybrid wind/PV/fuel cell power generation systems. Renewable Energy, 31(10), 1641–1656.

    Article  Google Scholar 

  51. Carley, S. (2009). State renewable energy electricity policies: An empirical evaluation of effectiveness. Energy Policy, 37(8), 3071–3081.

    Article  Google Scholar 

  52. Kneifel, J. (2010). Life-cycle carbon and cost analysis of energy efficiency measures in new commercial buildings. Energy and Buildings, 42(3), 333–340.

    Article  Google Scholar 

  53. Amann, M., Bertok, I., Borken-Kleefeld, J., Cofala, J., Heyes, C., Höglund-Isaksson, L., Klimont, Z., et al. (2011). Cost-effective control of air quality and greenhouse gases in Europe: Modeling and policy applications. Environmental Modelling & Software, 26(12), 1489–1501.

    Article  Google Scholar 

  54. Stevens, B., & Rose, A. (2002). A dynamic analysis of the marketable permits approach to global warming policy: A comparison of spatial and temporal flexibility. Journal of Environmental Economics and Management, 44(1), 45–69.

    Article  Google Scholar 

  55. Morrow, W. R., Gallagher, K. S., Collantes, G., & Lee, H. (2010). Analysis of policies to reduce oil consumption and greenhouse-gas emissions from the US transportation sector. Energy Policy, 38(3), 1305–1320.

    Article  Google Scholar 

  56. Vermont, B., & De Cara, S. (2010). How costly is mitigation of non-CO 2 greenhouse gas emissions from agriculture?: A meta-analysis. Ecological Economics, 69(7), 1373–1386.

    Article  Google Scholar 

  57. Agrawal, B., & Tiwari, G. N. (2010). Life cycle cost assessment of building integrated photovoltaic thermal (BIPVT) systems. Energy and Buildings, 42(9), 1472–1481.

    Article  Google Scholar 

  58. Chel, A., Tiwari, G. N., & Chandra, A. (2009). Simplified method of sizing and life cycle cost assessment of building integrated photovoltaic system. Energy and Buildings, 41(11), 1172.

    Article  Google Scholar 

  59. Marszal, A. J., & Heiselberg, P. (2011). Life cycle cost analysis of a multi-storey residential net zero energy building in Denmark. Energy, 36(9), 5600–5609.

    Article  Google Scholar 

  60. Basbagill, J., Flager, F., Lepech, M., & Fischer, M. (2013). Application of life-cycle assessment to early stage building design for reduced embodied environmental impacts. Building and Environment, 60, 81–92.

    Article  Google Scholar 

  61. Erlandsson, M., & Borg, M. (2003). Generic LCA-methodology applicable for buildings, constructions and operation services—today practice and development needs. Building and Environment, 38(7), 919–938.

    Article  Google Scholar 

  62. Hu, Z., Fang, F., Ben, D., Pu, G., & Wang, C. (2004). Net energy, CO 2 emission, and life-cycle cost assessment of cassava-based ethanol as an alternative automotive fuel in China. Applied Energy, 78(3), 247–256.

    Article  Google Scholar 

  63. Papong, S., & Malakul, P. (2010). Life-cycle energy and environmental analysis of bioethanol production from cassava in Thailand. Bioresource Technology, 101(1), S112–S118.

    Article  Google Scholar 

  64. Gluch, P., & Baumann, H. (2004). The life cycle costing (LCC) approach: a conceptual discussion of its usefulness for environmental decision-making. Building and Environment, 39(5), 571–580.

    Article  Google Scholar 

  65. Utne, I. B. (2009). Improving the environmental performance of the fishing fleet by use of Quality Function Deployment (QFD). Journal of Cleaner Production, 17(8), 724–731.

    Article  Google Scholar 

  66. Ness, B., Urbel-Piirsalu, E., Anderberg, S., & Olsson, L. (2007). Categorising tools for sustainability assessment. Ecological Economics, 60(3), 498–508.

    Article  Google Scholar 

  67. Luo, L., Van Der Voet, E., & Huppes, G. (2009). Life cycle assessment and life cycle costing of bioethanol from sugarcane in Brazil. Renewable and Sustainable Energy Reviews, 13(6), 1613–1619.

    Article  Google Scholar 

  68. Utne, I. B. (2009). Life cycle cost (LCC) as a tool for improving sustainability in the Norwegian fishing fleet. Journal of Cleaner Production, 17(3), 335–344.

    Article  Google Scholar 

  69. Nguyen, T. L. T., Gheewala, S. H., & Garivait, S. (2007). Energy balance and GHG-abatement cost of cassava utilization for fuel ethanol in Thailand. Energy Policy, 35(9), 4585–4596.

    Article  Google Scholar 

  70. Reijnders, L., & Huijbregts, M. A. (2007). Life cycle greenhouse gas emissions, fossil fuel demand and solar energy conversion efficiency in European bioethanol production for automotive purposes. Journal of Cleaner Production, 15(18), 1806–1812.

    Article  Google Scholar 

  71. Yan, X., & Crookes, R. J. (2009). Life cycle analysis of energy use and greenhouse gas emissions for road transportation fuels in China. Renewable and Sustainable Energy Reviews, 13(9), 2505–2514.

    Article  Google Scholar 

  72. Ugwu, O. O., Kumaraswamy, M. M., Kung, F., & Ng, S. T. (2005). Object-oriented framework for durability assessment and life cycle costing of highway bridges. Automation in Construction, 14(5), 611–632.

    Article  Google Scholar 

  73. González-García, S., Iribarren, D., Susmozas, A., Dufour, J., & Murphy, R. J. (2012). Life cycle assessment of two alternative bioenergy systems involving Salix spp. biomass: Bioethanol production and power generation. Applied Energy, 95, 111–122.

    Article  Google Scholar 

  74. Knapp, K., & Jester, T. (2001). Empirical investigation of the energy payback time for photovoltaic modules. Solar Energy, 71(3), 165–172.

    Article  Google Scholar 

  75. Peng, J., Lu, L., & Yang, H. (2013). Review on life cycle assessment of energy payback and greenhouse gas emission of solar photovoltaic systems. Renewable and Sustainable Energy Reviews, 19, 255–274.

    Article  Google Scholar 

  76. de Wild-Scholten, M. J. (2013). Energy payback time and carbon footprint of commercial photovoltaic systems. Solar Energy Materials and Solar Cells, 119, 296–305.

    Article  Google Scholar 

  77. Wilson, R., & Young, A. (1996). The embodied energy payback period of photovoltaic installations applied to buildings in the UK. Building and Environment, 31(4), 299–305.

    Article  Google Scholar 

  78. Goe, M., & Gaustad, G. (2014). Strengthening the case for recycling photovoltaics: An energy payback analysis. Applied Energy, 120, 41–48.

    Article  Google Scholar 

  79. Weißbach, D., Ruprecht, G., Huke, A., Czerski, K., Gottlieb, S., & Hussein, A. (2013). Energy intensities, EROIs (energy returned on invested), and energy payback times of electricity generating power plants. Energy, 52, 210–221.

    Article  Google Scholar 

  80. Gagnon, L., Belanger, C., & Uchiyama, Y. (2002). Life-cycle assessment of electricity generation options: the status of research in year 2001. Energy Policy, 30(14), 1267–1278.

    Article  Google Scholar 

  81. White, S. W., & Kulcinski, G. L. (2000). Birth to death analysis of the energy payback ratio and CO 2 gas emission rates from coal, fission, wind, and DT-fusion electrical power plants. Fusion Engineering and Design, 48(3), 473–481.

    Article  Google Scholar 

  82. Schleisner, L. (2000). Life cycle assessment of a wind farm and related externalities. Renewable Energy, 20(3), 279–288.

    Article  Google Scholar 

  83. Lu, L., & Yang, H. X. (2010). Environmental payback time analysis of a roof-mounted building-integrated photovoltaic (BIPV) system in Hong Kong. Applied Energy, 87(12), 3625–3631.

    Article  Google Scholar 

  84. Ardente, F., Beccali, G., Cellura, M., & Brano, V. L. (2005). Life cycle assessment of a solar thermal collector: sensitivity analysis, energy and environmental balances. Renewable Energy, 30(2), 109–130.

    Article  Google Scholar 

  85. Hang, Y., Qu, M., & Zhao, F. (2012). Economic and environmental life cycle analysis of solar hot water systems in the United States. Energy and Buildings, 45, 181–188.

    Article  Google Scholar 

  86. Wong, I. L., Eames, P. C., & Perera, R. S. (2007). A review of transparent insulation systems and the evaluation of payback period for building applications. Solar Energy, 81(9), 1058–1071.

    Article  Google Scholar 

  87. Chilton, J. C., Maidment, G. G., Marriott, D., Francis, A., & Tobias, G. (2000). Case study of a rainwater recovery system in a commercial building with a large roof. Urban water, 1(4), 345–354.

    Article  Google Scholar 

  88. Daouas, N., Hassen, Z., & Aissia, H. B. (2010). Analytical periodic solution for the study of thermal performance and optimum insulation thickness of building walls in Tunisia. Applied Thermal Engineering, 30(4), 319.

    Article  Google Scholar 

  89. Hacker, J. N., De Saulles, T. P., Minson, A. J., & Holmes, M. J. (2008). Embodied and operational carbon dioxide emissions from housing: a case study on the effects of thermal mass and climate change. Energy and Buildings, 40(3), 375–384.

    Article  Google Scholar 

  90. Staffell, I., Ingram, A., & Kendall, K. (2012). Energy and carbon payback times for solid oxide fuel cell based domestic CHP. International Journal of Hydrogen Energy, 37(3), 2509–2523.

    Article  Google Scholar 

  91. Kim, Y. J., & Sanders, G. L. (2002). Strategic actions in information technology investment based on real option theory. Decision Support Systems, 33(1), 1–11.

    Article  Google Scholar 

  92. McLellan, B. C., Corder, G. D., Giurco, D., & Green, S. (2009). Incorporating sustainable development in the design of mineral processing operations–Review and analysis of current approaches. Journal of Cleaner Production, 17(16), 1414–1425.

    Article  Google Scholar 

  93. Kjaerland, F. (2007). A real option analysis of investments in hydropower—The case of Norway. Energy Policy, 35(11), 5901–5908.

    Article  Google Scholar 

  94. Lee, S. C. (2011). Using real option analysis for highly uncertain technology investments: The case of wind energy technology. Renewable and Sustainable Energy Reviews, 15(9), 4443–4450.

    Article  Google Scholar 

  95. Yeo, K. T., & Qiu, F. (2003). The value of management flexibility—a real option approach to investment evaluation. International Journal of Project Management, 21(4), 243–250.

    Article  Google Scholar 

  96. Davis, G. A., & Owens, B. (2003). Optimizing the level of renewable electric R&D expenditures using real options analysis. Energy Policy, 31(15), 1589–1608.

    Article  Google Scholar 

  97. Kumbaroğlu, G., Madlener, R., & Demirel, M. (2008). A real options evaluation model for the diffusion prospects of new renewable power generation technologies. Energy Economics, 30(4), 1882–1908.

    Article  Google Scholar 

  98. Szolgayova, J., Fuss, S., & Obersteiner, M. (2008). Assessing the effects of CO 2 price caps on electricity investments—a real options analysis. Energy Policy, 36(10), 3974–3981.

    Article  Google Scholar 

  99. Jalil, A., & Mahmud, S. F. (2009). Environment Kuznets curve for CO 2 emissions: a cointegration analysis for China. Energy Policy, 37(12), 5167–5172.

    Article  Google Scholar 

  100. Miller, K. D., & Waller, H. G. (2003). Scenarios, real options and integrated risk management. Long Range Planning, 36(1), 93–107.

    Article  Google Scholar 

  101. Lander, D. M., & Pinches, G. E. (1998). Challenges to the practical implementation of modeling and valuing real options. The Quarterly Review of Economics and Finance, 38(3), 537–567.

    Article  Google Scholar 

  102. Kim, J., Hwang, M., Jeong, D. H., & Jung, H. (2012). Technology trends analysis and forecasting application based on decision tree and statistical feature analysis. Expert Systems with Applications, 39(16), 12618–12625.

    Article  Google Scholar 

  103. Hawkes, A. D., & Leach, M. A. (2007). Cost-effective operating strategy for residential micro-combined heat and power. Energy, 32(5), 711–723.

    Article  Google Scholar 

  104. Tso, G. K., & Yau, K. K. (2007). Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks. Energy, 32(9), 1761–1768.

    Article  Google Scholar 

  105. Weiner, D., Fisher, D., Moses, E. J., Katz, B., & Meron, G. (2001). Operation experience of a solar-and wind-powered desalination demonstration plant. Desalination, 137(1), 7–13.

    Article  Google Scholar 

  106. Lee, S. C., & Shih, L. H. (2010). Renewable energy policy evaluation using real option model—The case of Taiwan. Energy Economics, 32, S67–S78.

    Article  Google Scholar 

  107. Biswal, M., & Dash, P. K. (2013). Detection and characterization of multiple power quality disturbances with a fast S-transform and decision tree based classifier. Digital Signal Processing, 23(4), 1071–1083.

    Article  Google Scholar 

  108. Cramer, G. M., Ford, R. A., & Hall, R. L. (1976). Estimation of toxic hazard—a decision tree approach. Food and Cosmetics Toxicology, 16(3), 255–276.

    Article  Google Scholar 

  109. Duncan, R. (1980). What is the right organization structure? Decision tree analysis provides the answer. Organizational Dynamics, 7(3), 59–80.

    Article  Google Scholar 

  110. Yu, Z., Haghighat, F., Fung, B. C., & Yoshino, H. (2010). A decision tree method for building energy demand modeling. Energy and Buildings, 42(10), 1637–1646.

    Article  Google Scholar 

  111. Olaru, C., & Wehenkel, L. (2003). A complete fuzzy decision tree technique. Fuzzy Sets and Systems, 138(2), 221–254.

    Article  Google Scholar 

  112. Varma, V. A., Reklaitis, G. V., Blau, G. E., & Pekny, J. F. (2007). Enterprise-wide modeling & optimization—An overview of emerging research challenges and opportunities. Computers & Chemical Engineering, 31(5), 692–711.

    Article  Google Scholar 

  113. Wallace, S. W., & Fleten, S. E. (2003). Stochastic programming models in energy. InHandbooks in operations research and management science (Vol. 10, pp. 637–677). Amsterdam: Elsevier.

    Google Scholar 

  114. Pehnt, M. (2006). Dynamic life cycle assessment (LCA) of renewable energy technologies. Renewable Energy, 31(1), 55–71.

    Article  Google Scholar 

  115. Liu, B. T., Chien, K. H., & Wang, C. C. (2004). Effect of working fluids on organic Rankine cycle for waste heat recovery. Energy, 29(8), 1207–1217.

    Article  Google Scholar 

  116. Reiche, D., & Bechberger, M. (2004). Policy differences in the promotion of renewable energies in the EU member states. Energy Policy, 32(7), 843–849.

    Article  Google Scholar 

  117. Walker, G., & Devine-Wright, P. (2008). Community renewable energy: What should it mean? Energy Policy, 36(2), 497–500.

    Article  Google Scholar 

  118. Yu, Z., Fung, B. C., Haghighat, F., Yoshino, H., & Morofsky, E. (2011). A systematic procedure to study the influence of occupant behavior on building energy consumption. Energy and Buildings, 43(6), 1409–1417.

    Article  Google Scholar 

  119. Richardson, B. C. (2005). Sustainable transport: analysis frameworks. Journal of Transport Geography, 13(1), 29–39.

    Article  Google Scholar 

  120. Lund, H., & Salgi, G. (2009). The role of compressed air energy storage (CAES) in future sustainable energy systems. Energy Conversion and Management, 50(5), 1172–1179.

    Article  Google Scholar 

  121. Lund, H. (2005). Large-scale integration of wind power into different energy systems. Energy, 30(13), 2402–2412.

    Article  Google Scholar 

  122. Keeney, R., & Raiffa, H. (1976). Decisions with Multiple Objectives: Preferences and Value Tradeoffs. New York: Willey.

    Google Scholar 

  123. Sanayei, A., Mousavi, S. F., Abdi, M. R., & Mohaghar, A. (2008). An integrated group decision-making process for supplier selection and order allocation using multi-attribute utility theory and linear programming. Journal of the Franklin Institute, 345(7), 731–747.

    Article  Google Scholar 

  124. Bose, U., Davey, A. M., & Olson, D. L. (1997). Multi-attribute utility methods in group decision making: past applications and potential for inclusion in GDSS. Omega, 25(6), 691–706.

    Article  Google Scholar 

  125. Keeney, R. L. (1977). The art of assessing multiattribute utility functions. Organizational Behavior and Human Performance, 19(2), 267–310.

    Article  Google Scholar 

  126. Ananda, J., & Herath, G. (2005). Evaluating public risk preferences in forest land-use choices using multi-attribute utility theory. Ecological Economics, 55(3), 408–419.

    Article  Google Scholar 

  127. Zanakis, S. H., Solomon, A., Wishart, N., & Dublish, S. (1998). Multi-attribute decision making: A simulation comparison of select methods. European Journal of Operational Research, 107(3), 507–529.

    Article  Google Scholar 

  128. Konidari, P., & Mavrakis, D. (2007). A multi-criteria evaluation method for climate change mitigation policy instruments. Energy Policy, 35(12), 6235–6257.

    Article  Google Scholar 

  129. Holt, G. D. (1998). Which contractor selection methodology? International Journal of Project Management, 16(3), 153–164.

    Article  Google Scholar 

  130. Pawlak, Z., & Sowinski, R. (1994). Rough set approach to multi-attribute decision analysis. European Journal of Operational Research, 72(3), 443–459.

    Article  Google Scholar 

  131. Kainuma, Y., & Tawara, N. (2006). A multiple attribute utility theory approach to lean and green supply chain management. International Journal of Production Economics, 101(1), 99–108.

    Article  Google Scholar 

  132. Kowalski, K., Stagl, S., Madlener, R., & Omann, I. (2009). Sustainable energy futures: Methodological challenges in combining scenarios and participatory multi-criteria analysis. European Journal of Operational Research, 197(3), 1063–1074.

    Article  Google Scholar 

  133. Anandalingam, G., & Olsson, C. E. (1989). A multi-stage multi-attribute decision model for project selection. European Journal of Operational Research, 43(3), 271–283.

    Article  Google Scholar 

  134. Velasquez, M., & Hester, P. T. (2013). An analysis of multi-criteria decision making methods. International Journal of Operations Research, 10(2), 56–66.

    Google Scholar 

  135. Elkarmi, F., & Mustafa, I. (1993). Increasing the utilization of solar energy technologies (SET) in Jordan: Analytic Hierarchy Process. Energy Policy, 21(9), 978–984.

    Article  Google Scholar 

  136. Forman, E. H., & Gass, S. I. (2001). The analytic hierarchy process-an exposition. Operations Research, 49(4), 469–486.

    Article  Google Scholar 

  137. Saaty, T. L. (1980). The analytic hierarchy process: planning, priority setting, resource allocation. McGrawHill, New York.

    Google Scholar 

  138. Kocaoglu, D. F. (1983). A participative approach to program evaluation. IEEE Transactions on Engineering Management, EM-30, 112–118.

    Article  Google Scholar 

  139. Lee, S. K., Yoon, Y. J., & Kim, J. W. (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.

    Article  Google Scholar 

  140. Ramanathan, R., & Ganesh, L. S. (1995). Energy alternatives for lighting in households: an evaluation using an integrated goal programming-AHP model. Energy, 20(1), 63–72.

    Article  Google Scholar 

  141. Lee, S. K., Mogi, G., & Kim, J. W. (2008). The competitiveness of Korea as a developer of hydrogen energy technology: the AHP approach. Energy Policy, 36(4), 1284–1291.

    Article  Google Scholar 

  142. Hämäläinen, R. P., & Seppäläinen, T. O. (1986). The analytic network process in energy policy planning. Socio-Economic Planning Sciences, 20(6), 399–405.

    Article  Google Scholar 

  143. Kaya, T., & Kahraman, C. (2010). Multicriteria renewable energy planning using an integrated fuzzy VIKOR & AHP methodology: The case of Istanbul. Energy, 35(6), 2517–2527.

    Article  Google Scholar 

  144. Heo, E., Kim, J., & Boo, K. J. (2010). Analysis of the assessment factors for renewable energy dissemination program evaluation using fuzzy AHP. Renewable and Sustainable Energy Reviews, 14(8), 2214–2220.

    Article  Google Scholar 

  145. Poh, K. L., & Ang, B. W. (1999). Transportation fuels and policy for Singapore: an AHP planning approach. Computers & Industrial Engineering, 37(3), 507–525.

    Article  Google Scholar 

  146. Ramanathan, R. (2001). A note on the use of the analytic hierarchy process for environmental impact assessment. Journal of Environmental Management, 63(1), 27–35.

    Article  Google Scholar 

  147. Ulutaş, B. H. (2005). Determination of the appropriate energy policy for Turkey. Energy, 30(7), 1146–1161.

    Article  Google Scholar 

  148. Köne, A. C., & Büke, T. (2007). An Analytical Network Process (ANP) evaluation of alternative fuels for electricity generation in Turkey. Energy Policy, 35(10), 5220–5228.

    Article  Google Scholar 

  149. Erdoğmuş, S., Aras, H., & Koç, E. (June 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.

    Article  Google Scholar 

  150. İ.Yüksel, and M.Dagdeviren. (2007). Using the analytic network process (ANP) in a SWOT analysis–A case study for a textile firm. Information Sciences, 177(16), 3364–3382.

    Article  Google Scholar 

  151. Aragonés-Beltrán, P., Chaparro-González, F., Pastor-Ferrando, J. P., & Rodríguez-Pozo, F. (2010). An ANP-based approach for the selection of photovoltaic solar power plant investment projects. Renewable and Sustainable Energy Reviews, 14(1), 249–264.

    Article  Google Scholar 

  152. Xu, P., & Chan, E. H. (2013). ANP model for sustainable Building Energy Efficiency Retrofit (BEER) using Energy Performance Contracting (EPC) for hotel buildings in China. Habitat International, 37, 104–112.

    Article  Google Scholar 

  153. Hung, S. J. (2011). Activity-based divergent supply chain planning for competitive advantage in the risky global environment: A DEMATEL-ANP fuzzy goal programming approach. Expert Systems with Applications, 38(8), 9053–9062.

    Article  Google Scholar 

  154. Sarkis, J. (1998). Evaluating environmentally conscious business practices. European Journal of Operational Research, 107(1), 159–174.

    Article  Google Scholar 

  155. Theißen, S., & Spinler, S. (2014). Strategic analysis of manufacturer-supplier partnerships: An ANP model for collaborative CO 2 reduction management. European Journal of Operational Research, 233(2), 383–397.

    Article  Google Scholar 

  156. Shiue, Y. C., & Lin, C. Y. (2012). Applying analytic network process to evaluate the optimal recycling strategy in upstream of solar energy industry. Energy and Buildings, 54, 266–277.

    Article  Google Scholar 

  157. Mazurek, J., & Kiszová, Z. (2012). Modeling dependence and feedback in ANP with fuzzy cognitive maps. InProceedings of 30th International Conference Mathematical Methods in Economics (pp. 558–563).

    Google Scholar 

  158. Turcksin, L., Bernardini, A., & Macharis, C. (2011). A combined AHP-PROMETHEE approach for selecting the most appropriate policy scenario to stimulate a clean vehicle fleet. Procedia-Social and Behavioral Sciences, 20, 954–965.

    Article  Google Scholar 

  159. Tsoutsos, T., Drandaki, M., Frantzeskaki, N., Iosifidis, E., & Kiosses, I. (2009). Sustainable energy planning by using multi-criteria analysis application in the island of Crete. Energy Policy, 37(5), 1587–1600.

    Article  Google Scholar 

  160. Haralambopoulos, D. A., & Polatidis, H. (2003). Renewable energy projects: structuring a multi-criteria group decision-making framework. Renewable Energy, 28(6), 961–973.

    Article  Google Scholar 

  161. Madlener, R., Kowalski, K., & Stagl, S. (2007). New ways for the integrated appraisal of national energy scenarios: the case of renewable energy use in Austria. Energy Policy, 35(12), 6060–6074.

    Article  Google Scholar 

  162. Goumas, M. G., Lygerou, V. A., & Papayannakis, L. E. (1999). Computational methods for planning and evaluating geothermal energy projects. Energy Policy, 27(3), 147–154.

    Article  Google Scholar 

  163. Cavallaro, F., & Ciraolo, L. (2005). A multicriteria approach to evaluate wind energy plants on an Italian island. Energy Policy, 33(2), 235–244.

    Article  Google Scholar 

  164. Goumas, M., & Lygerou, V. (2000). An extension of the PROMETHEE method for decision making in fuzzy environment: Ranking of alternative energy exploitation projects. European Journal of Operational Research, 123(3), 606–613.

    Article  Google Scholar 

  165. Ren, H., Gao, W., Zhou, W., & Nakagami, K. I. (2009). Multi-criteria evaluation for the optimal adoption of distributed residential energy systems in Japan. Energy Policy, 37(12), 5484–5493.

    Article  Google Scholar 

  166. Cavallaro, F. (2009). Multi-criteria decision aid to assess concentrated solar thermal technologies. Renewable Energy, 34(7), 1678–1685.

    Article  Google Scholar 

  167. Beynon, M. J., & Wells, P. (2008). The lean improvement of the chemical emissions of motor vehicles based on preference ranking: A PROMETHEE uncertainty analysis. Omega, 36(3), 384–394.

    Article  Google Scholar 

  168. Papadopoulos, A., & Karagiannidis, A. (2008). Application of the multi-criteria analysis method Electre III for the optimisation of decentralised energy systems. Omega, 36(5), 766–776.

    Article  Google Scholar 

  169. Cavallaro, F. (2010). A comparative assessment of thin-film photovoltaic production processes using the ELECTRE III method. Energy Policy, 38(1), 463–474.

    Article  Google Scholar 

  170. Georgopoulou, E., Lalas, D., & Papagiannakis, L. (1997). A multicriteria decision aid approach for energy planning problems: The case of renewable energy option. European Journal of Operational Research, 103(1), 38–54.

    Article  Google Scholar 

  171. Huang, J. P., Poh, K. L., & Ang, B. W. (1995). Decision analysis in energy and environmental modeling. Energy, 20(9), 843–855.

    Article  Google Scholar 

  172. Kahraman, C., & Kaya, İ. (2010). A fuzzy multicriteria methodology for selection among energy alternatives. Expert Systems with Applications, 37(9), 6270–6281.

    Article  Google Scholar 

  173. Neves, L. P., Martins, A. G., Antunes, C. H., & Dias, L. C. (2008). A multi-criteria decision approach to sorting actions for promoting energy efficiency. Energy Policy, 36(7), 2351–2363.

    Article  Google Scholar 

  174. Topcu, Y. I., & Ulengin, F. (2004). Energy for the future: An integrated decision aid for the case of Turkey. Energy, 29(1), 137–154.

    Article  Google Scholar 

  175. Shen, Y. C., Lin, G. T., Li, K. P., & Yuan, B. J. (2010). An assessment of exploiting renewable energy sources with concerns of policy and technology. Energy Policy, 38(8), 4604–4616.

    Article  Google Scholar 

  176. Milani, A. S., Shanian, A., & El-Lahham, C. (2006). Using different ELECTRE methods in strategic planning in the presence of human behavioral resistance. Journal of Applied Mathematics and Decision Sciences, 2006, 1–19.

    Article  Google Scholar 

  177. Crawley, D. B., Hand, J. W., Kummert, M., & Griffith, B. T. (2008). Contrasting the capabilities of building energy performance simulation programs. Building and Environment, 43(4), 661–673.

    Article  Google Scholar 

  178. Yerramalla, S., Davari, A., Feliachi, A., & Biswas, T. (2003). Modeling and simulation of the dynamic behavior of a polymer electrolyte membrane fuel cell. Journal of Power Sources, 124(1), 104–113.

    Article  Google Scholar 

  179. Markel, T., Brooker, A., Hendricks, T., Johnson, V., Kelly, K., Kramer, B., O’Keefe, M., Sprik, S., & Wipke, K. (2002). ADVISOR: a systems analysis tool for advanced vehicle modeling. Journal of Power Sources, 110(2), 255–266.

    Article  Google Scholar 

  180. Daim, T. U., Rueda, G., Martin, H., & Gerdsri, P. (2006). Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological Forecasting and Social Change, 73(8), 981–1012.

    Article  Google Scholar 

  181. Shah, R. C., Roy, S., Jain, S., & Brunette, W. (2003). Data mules: Modeling and analysis of a three-tier architecture for sparse sensor networks. Ad Hoc Networks, 1(2), 215–233.

    Article  Google Scholar 

  182. Swan, L. G., & Ugursal, V. I. (2009). Modeling of end-use energy consumption in the residential sector: A review of modeling techniques. Renewable and Sustainable Energy Reviews, 13(8), 1819–1835.

    Article  Google Scholar 

  183. Kellner, M. I., Madachy, R. J., & Raffo, D. M. (1999). Software process simulation modeling: why? what? how? Journal of Systems and Software, 46(2), 91–105.

    Article  Google Scholar 

  184. Perez, R., Ineichen, P., Seals, R., Michalsky, J., & Stewart, R. (1990). Modeling daylight availability and irradiance components from direct and global irradiance. Solar Energy, 44(5), 271–289.

    Article  Google Scholar 

  185. Fong, K. F., Hanby, V. I., & Chow, T. T. (2006). HVAC system optimization for energy management by evolutionary programming. Energy and Buildings, 38(3), 220–231.

    Article  Google Scholar 

  186. Cai, Y. P., Huang, G. H., Lin, Q. G., Nie, X. H., & Tan, Q. (2009). An optimization-model-based interactive decision support system for regional energy management systems planning under uncertainty. Expert Systems with Applications, 36(2), 3470–3482.

    Article  Google Scholar 

  187. Yang, H., Zhou, W., Lu, L., & Fang, Z. (2008). Optimal sizing method for stand-alone hybrid solar–wind system with LPSP technology by using genetic algorithm. Solar Energy, 82(4), 354–367.

    Article  Google Scholar 

  188. Li, C. H., Zhu, X. J., Cao, G. Y., Sui, S., & Hu, M. R. (2009). Dynamic modeling and sizing optimization of stand-alone photovoltaic power systems using hybrid energy storage technology. Renewable Energy, 34(3), 815–826.

    Article  Google Scholar 

  189. Ulleberg, Ø. (2003). Modeling of advanced alkaline electrolyzers: a system simulation approach. International Journal of Hydrogen Energy, 28(1), 21–33.

    Article  Google Scholar 

  190. Banos, R., Manzano-Agugliaro, F., Montoya, F. G., Gil, C., Alcayde, A., & Gómez, J. (2011). Optimization methods applied to renewable and sustainable energy: A review. Renewable and Sustainable Energy Reviews, 15(4), 1753–1766.

    Article  Google Scholar 

  191. Ren, H., Gao, W., & Ruan, Y. (2009). Economic optimization and sensitivity analysis of photovoltaic system in residential buildings. Renewable Energy, 34(3), 883–889.

    Article  Google Scholar 

  192. Benonysson, A., Bøhm, B., & Ravn, H. F. (1995). Operational optimization in a district heating system. Energy Conversion and Management, 36(5), 297–314.

    Article  Google Scholar 

  193. Münster, M., & Meibom, P. (2011). Optimization of use of waste in the future energy syste. Energy, 36(3), 1612–1622.

    Article  Google Scholar 

  194. Cavallaro, F. (2010). Fuzzy TOPSIS approach for assessing thermal-energy storage in concentrated solar power (CSP) systems. Applied Energy, 87(2), 496–503.

    Article  Google Scholar 

  195. Behzadian, M., Otaghsara, S. K., Yazdani, M., & Ignatius, J. (2012). A state-of the-art survey of TOPSIS applications. Expert Systems with Applications, 39(17), 13051–13069.

    Article  Google Scholar 

  196. Chamodrakas, I., & Martakos, D. (2012). A utility-based fuzzy TOPSIS method for energy efficient network selection in heterogeneous wireless networks. Applied Soft Computing, 12(7), 1929–1938.

    Article  Google Scholar 

  197. Sadeghzadeh, K., & Salehi, M. B. (2011). Mathematical analysis of fuel cell strategic technologies development solutions in the automotive industry by the TOPSIS multi-criteria decision making method. International Journal of Hydrogen Energy, 36(20), 13272–13280.

    Article  Google Scholar 

  198. Yan, G., Ling, Z., & Dequn, Z. (2011). Performance evaluation of coal enterprises energy conservation and reduction of pollutant emissions base on GRD-TOPSIS. Energy Procedia, 5, 535–539.

    Article  Google Scholar 

  199. Choudhary, D., & Shankar, R. (2012). An STEEP-fuzzy AHP-TOPSIS framework for evaluation and selection of thermal power plant location: A case study from India. Energy, 42(1), 510–521.

    Article  Google Scholar 

  200. Wang, E. (2015). Benchmarking whole-building energy performance with multi-criteria technique for order preference by similarity to ideal solution using a selective objective-weighting approach. Applied Energy, 146, 92–103.

    Article  Google Scholar 

  201. Bas, E. (2013). The integrated framework for analysis of electricity supply chain using an integrated SWOT-fuzzy TOPSIS methodology combined with AHP: The case of Turkey. International Journal of Electrical Power & Energy Systems, 44(1), 897–907.

    Article  Google Scholar 

  202. Haehnlein, S., Bayer, P., & Blum, P. (2010). International legal status of the use of shallow geothermal energy. Renewable and Sustainable Energy Reviews, 14(9), 2611–2625.

    Article  Google Scholar 

  203. Daim, T. U., Kayakutlu, G., & Cowan, K. (2010). Developing Oregon’s renewable energy portfolio using fuzzy goal programming model. Computers & Industrial Engineering, 59(4), 786–793.

    Article  Google Scholar 

  204. Energyhomes.Org. (2008). Leadership in energy and environmental design. Centennial.

    Google Scholar 

  205. Kraan, C. M. (2013). Drivers and barriers to deep geothermal energy in the Netherlands: What are the implications of government policy?, M.Sc, University of Edinburgh, School of Geosciences.

    Google Scholar 

  206. Geothermal Basics – Power Plant Costs, Geo-energy.org. (2016). [Online]. Available: http://geo-energy.org/geo_basics_plant_cost.aspx. Accessed: 29 May 2016.

  207. Rybach, L. (2003). Geothermal energy: sustainability and the environment. Geothermics, 32(4), 463–470.

    Article  Google Scholar 

  208. Northwest Energy Efficiency Task Force, Nwcouncil.org. (2016). [Online]. Available: http://www.nwcouncil.org/energy/neet/home/. Accessed 29 May 2016.

  209. State of Oregon, Employment Department, Population growth rate increases in Oregon for third straight year, W. Burchard, August 2015.

    Google Scholar 

  210. Shah, V. P., Debella, D. C., & Ries, R. J. (2008). Life cycle assessment of residential heating and cooling systems in four regions in the United States. Energy and Buildings, 40(4), 503–513.

    Article  Google Scholar 

  211. Newsham, G. R., & Bowker, B. G. (2010). The effect of utility time-varying pricing and load control strategies on residential summer peak electricity use: a review. Energy Policy, 38(7), 3289–3296.

    Article  Google Scholar 

  212. Saidur, R. (2010). A review on electrical motors energy use and energy savings. Renewable and Sustainable Energy Reviews, 14(3), 877–898.

    Article  Google Scholar 

  213. Regnier, E. (2007). Oil and energy price volatility. Energy Economics, 29(3), 405–427.

    Article  Google Scholar 

  214. Kaygusuz, K., & Kaygusuz, A. (2004). Geothermal energy in Turkey: the sustainable future. Renewable and Sustainable Energy Reviews, 8(6), 545–563.

    Article  Google Scholar 

  215. Geothermal Energy Association. (2015). Annual U.S. & Global Geothermal Power Production Report, Geo-Energy.Org.

    Google Scholar 

  216. Daim, T., Yates, D., Peng, Y., & Jimenez, B. (August 2009). Technology assessment for clean energy technologies: the case of the Pacific Northwest. Technology in Society, 31(3), 232–243.

    Article  Google Scholar 

  217. Gerdsri, P., & Kocaoglu, D. (2009). A systematic approach to developing national technology policy and strategy for emerging technologies: A case study of nanotechnology for Thailand's agriculture industry. InIn Management of Engineering & Technology, 2009. PICMET 2009. Portland International Conference (pp. 447–461).

    Chapter  Google Scholar 

  218. Hämäläinen, R. P., & Karjalainen, R. (1992). Decision support for risk analysis in energy policy. European Journal of Operational Research, 56(2), 172–183.

    Article  Google Scholar 

  219. Gerdsri, P., & Kocaoglu, D. F. (2008). HDM for developing national emerging technology strategy and policy supporting sustainable economy: a case study of nanotechnology for Thailand’s agriculture. InManagement of Engineering & Technology, 2008. PICMET 2008. Portland International Conference (pp. 344–350).

    Chapter  Google Scholar 

  220. Geothermal Technologies Program, D. K. Garman, U.S. Department of Energy, Energy Efficiency and Renewable Energy.

    Google Scholar 

  221. Fridleifsson, I. B. (2001). Geothermal energy for the benefit of the people. Renewable and Sustainable Energy Reviews, 5(3), 299–312.

    Article  Google Scholar 

  222. Tourkolias, C., & Mirasgedis, S. (2011). Quantification and monetization of employment benefits associated with renewable energy technologies in Greece. Renewable and Sustainable Energy Reviews, 15(6), 2876–2886.

    Article  Google Scholar 

  223. Agioutantis, Z., & Bekas, A. (2000). The potential of district heating using geothermal energy. A case study, Greece. Geothermics, 29(1), 51–64.

    Article  Google Scholar 

  224. Chatzimouratidis, A. I., & Pilavachi, P. A. (2008). Multicriteria evaluation of power plants impact on the living standard using the analytic hierarchy process. Energy Policy, 36(3), 1074–1089.

    Article  Google Scholar 

  225. Lehr, U., Nitsch, J., Kratzat, M., Lutz, C., & Edler, D. (2008). Renewable energy and employment in Germany. Energy Policy, 36(1), 108–117.

    Article  Google Scholar 

  226. Geothermal Rules Encourage AlternativeEnergy Development on Federal Lands, Doi.gov. (2016). [Online]. Available: https://www.doi.gov/sites/doi.gov/files/archive/news/archive/06_News_Releases/060721a.htm. Accessed 01 Jun 2016.

  227. Geothermal Energy, US Department of interior, Bureau of land management.

    Google Scholar 

  228. Geothermal Energy Why it matters to Oregon, Oregon Department of Energy, Salem, December 2014.

    Google Scholar 

  229. Evans, A., Strezov, V., & Evans, T. J. (2009). Assessment of sustainability indicators for renewable energy technologies. Renewable and Sustainable Energy Reviews, 13(5), 1082–1088.

    Article  Google Scholar 

  230. Hondo, H. (2005). Life cycle GHG emission analysis of power generation systems: Japanese case. Energy, 30(11), 2042–2056.

    Article  Google Scholar 

  231. Panwar, N. L., Kaushik, S. C., & Kothari, S. (2011). Role of renewable energy sources in environmental protection: a review. Renewable and Sustainable Energy Reviews, 15(3), 1513–1524.

    Article  Google Scholar 

  232. Kaygusuz, K. (2009). Energy and environmental issues relating to greenhouse gas emissions for sustainable development in Turkey. Renewable and Sustainable Energy Reviews, 13(1), 253–270.

    Article  Google Scholar 

  233. Bayer, P., Saner, D., Bolay, S., Rybach, L., & Blum, P. (2012). Greenhouse gas emission savings of ground source heat pump systems in Europe: A review. Renewable and Sustainable Energy Reviews, 16(2), 1256–1267.

    Article  Google Scholar 

  234. Jacobson, M. Z., & Delucchi, M. A. (2011). Providing all global energy with wind, water, and solar power, Part I: Technologies, energy resources, quantities and areas of infrastructure, and materials. Energy Policy, 39(3), 1154–1169.

    Article  Google Scholar 

  235. Abbasi, S. A., & Abbasi, N. (2000). The likely adverse environmental impacts of renewable energy sources. Applied Energy, 65(1), 121–144.

    Article  Google Scholar 

  236. De Vries, B. J., Van Vuuren, D. P., & Hoogwijk, M. M. (2007). Renewable energy sources: Their global potential for the first-half of the 21st century at a global level: An integrated approach. Energy Policy, 35(4), 2590–2610.

    Article  Google Scholar 

  237. Mahmoudi, H., Spahis, N., Goosen, M. F., Ghaffour, N., Drouiche, N., & Ouagued, A. (2010). Application of geothermal energy for heating and fresh water production in a brackish water greenhouse desalination unit: A case study from Algeria. Renewable and Sustainable Energy Reviews, 14(1), 512–517.

    Article  Google Scholar 

  238. Fehler, M. C. (1989). Stress control of seismicity patterns observed during hydraulic fracturing experiments at the Fenton Hill Hot Dry Rock geothermal energy site, New Mexico. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 26(3), 211–219.

    Article  Google Scholar 

  239. Pine, R. J., & Batchelor, A. S. (1984). Downward migration of shearing in jointed rock during hydraulic injections. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 21(5), 249–263.

    Article  Google Scholar 

  240. Evans, K. F., Zappone, A., Kraft, T., Deichmann, N., & Moia, F. (2012). A survey of the induced seismic responses to fluid injection in geothermal and CO 2 reservoirs in Europe. Geothermics, 41, 30–54.

    Article  Google Scholar 

  241. Majer, E. L., & Peterson, J. E. (2007). The impact of injection on seismicity at The Geysers, California Geothermal Field. International Journal of Rock Mechanics and Mining Sciences, 44(8), 1079–1090.

    Article  Google Scholar 

  242. Charléty, J., Cuenot, N., Dorbath, L., Dorbath, C., Haessler, H., & Frogneux, M. (2007). Large earthquakes during hydraulic stimulations at the geothermal site of Soultz-sous-Forêts. International Journal of Rock Mechanics and Mining Sciences, 44(8), 1091–1105.

    Article  Google Scholar 

  243. Chang, J., Leung, D. Y., Wu, C. Z., & Yuan, Z. H. (2003). A review on the energy production, consumption, and prospect of renewable energy in China. Renewable and Sustainable Energy Reviews, 7(5), 453–468.

    Article  Google Scholar 

  244. Asif, M., & Muneer, T. (2007). Energy supply, its demand and security issues for developed and emerging economies. Renewable and Sustainable Energy Reviews, 11(7), 1388–1413.

    Article  Google Scholar 

  245. Akella, A. K., Saini, R. P., & Sharma, M. P. (2009). Social, economical and environmental impacts of renewable energy systems. Renewable Energy, 34(2), 390–396.

    Article  Google Scholar 

  246. Omer, A. M. (2008). Energy, environment and sustainable development. Renewable and Sustainable Energy Reviews, 12(9), 2265–2300.

    Article  Google Scholar 

  247. Shuit, S. H., Tan, K. T., Lee, K. T., & Kamaruddin, A. H. (2009). Oil palm biomass as a sustainable energy source: a Malaysian case study. Energy, 34(9), 1225–1235.

    Article  Google Scholar 

  248. Bilgen, S., Keleş, S., Kaygusuz, A., Sarı, A., & Kaygusuz, K. (2008). Global warming and renewable energy sources for sustainable development: a case study in Turkey. Renewable and Sustainable Energy Reviews, 12(2), 372–396.

    Article  Google Scholar 

  249. Fridleifsson, I. B. (2003). Status of geothermal energy amongst the world's energy sources. Geothermics, 32(4), 379–388.

    Article  Google Scholar 

  250. Evrendilek, F., & Ertekin, C. (2003). Assessing the potential of renewable energy sources in Turkey. Renewable Energy, 28(15), 2303–2315.

    Article  Google Scholar 

  251. Kumar, A., Kumar, K., Kaushik, N., Sharma, S., & Mishra, S. (2010). Renewable energy in India: current status and future potentials. Renewable and Sustainable Energy Reviews, 14(8), 2434–2442.

    Article  Google Scholar 

  252. Østergaard, P. A., & Lund, H. (2011). A renewable energy system in Frederikshavn using low-temperature geothermal energy for district heating. Applied Energy, 88(2), 479–487.

    Article  Google Scholar 

  253. Rezaie, B., & Rosen, M. A. (2012). District heating and cooling: Review of technology and potential enhancements. Applied Energy, 93, 2–10.

    Article  Google Scholar 

  254. Chamorro, C. R., Mondéjar, M. E., Ramos, R., Segovia, J. J., Martín, M. C., & Villamañán, M. A. (2012). World geothermal power production status: Energy, environmental and economic study of high enthalpy technologies. Energy, 42(1), 10–18.

    Article  Google Scholar 

  255. Lund, J. W., Freeston, D. H., & Boyd, T. L. (2011). Direct utilization of geothermal energy 2010 worldwide review. Geothermics, 40(3), 159–180.

    Article  Google Scholar 

  256. Ball, M., & Wietschel, M. (2009). The future of hydrogen–opportunities and challenges. International Journal of Hydrogen Energy, 34(2), 615–627.

    Article  Google Scholar 

  257. Crompton, P., & Wu, Y. (2005). Energy consumption in China: past trends and future directions. Energy Economics, 27(1), 195–208.

    Article  Google Scholar 

  258. Dorian, J. P., Franssen, H. T., & Simbeck, D. R. (2006). Global challenges in energy. Energy Policy, 34(15), 1984–1991.

    Article  Google Scholar 

  259. Franco, A., & Diaz, A. R. (2009). The future challenges for “clean coal technologies”: joining efficiency increase and pollutant emission control. Energy, 34(3), 348–354.

    Article  Google Scholar 

  260. Oh, T. H., Pang, S. Y., & Chua, S. C. (2010). Energy policy and alternative energy in Malaysia: issues and challenges for sustainable growth. Renewable and Sustainable Energy Reviews, 14(4), 1241–1252.

    Article  Google Scholar 

  261. Stiegel, G. J., & Ramezan, M. (2006). Hydrogen from coal gasification: an economical pathway to a sustainable energy future. International Journal of Coal Geology, 65(3), 173–190.

    Article  Google Scholar 

  262. Dincer, I. (2000). Renewable energy and sustainable development: a crucial review. Renewable and Sustainable Energy Reviews, 4(2), 157–175.

    Article  Google Scholar 

  263. Momirlan, M., & Veziroglu, T. N. (2005). The properties of hydrogen as fuel tomorrow in sustainable energy system for a cleaner planet. International Journal of Hydrogen Energy, 30(7), 795–802.

    Article  Google Scholar 

  264. Lund, J. W., Freeston, D. H., & Boyd, T. L. (2005). Direct application of geothermal energy: 2005 worldwide review. Geothermics, 34(6), 691–727.

    Article  Google Scholar 

  265. Chiodini, G., & Cioni, R. (1989). Gas geobarometry for hydrothermal systems and its application to some Italian geothermal areas. Applied Geochemistry, 4(5), 465–472.

    Article  Google Scholar 

  266. Hepbasli, A., & Akdemir, O. (2004). Energy and exergy analysis of a ground source (geothermal) heat pump system. Energy Conversion and Management, 45(5), 737–753.

    Article  Google Scholar 

  267. Casas, W., & Schmitz, G. (2005). Experiences with a gas driven, desiccant assisted air conditioning system with geothermal energy for an office building. Energy and Buildings, 37(5), 493–501.

    Article  Google Scholar 

  268. Sanner, B., Karytsas, C., Mendrinos, D., & Rybach, L. (2003). Current status of ground source heat pumps and underground thermal energy storage in Europe. Geothermics, 32(4), 579–588.

    Article  Google Scholar 

  269. Cai, W. G., Wu, Y., Zhong, Y., & Ren, H. (2009). China building energy consumption: situation, challenges and corresponding measures. Energy Policy, 37(6), 2054–2059.

    Article  Google Scholar 

  270. Hähnlein, S., Bayer, P., Ferguson, G., & Blum, P. (2013). Sustainability and policy for the thermal use of shallow geothermal energy. Energy Policy, 59, 914–925.

    Article  Google Scholar 

  271. Allen, A., Milenic, D., & Sikora, P. (2003). Shallow gravel aquifers and the urban ‘heat island’effect: a source of low enthalpy geothermal energy. Geothermics, 32(4), 569–578.

    Article  Google Scholar 

  272. Stambouli, A. B., Khiat, Z., Flazi, S., & Kitamura, Y. (2012). A review on the renewable energy development in Algeria: current perspective, energy scenario and sustainability issues. Renewable and Sustainable Energy Reviews, 16(7), 4445–4460.

    Article  Google Scholar 

  273. Akorede, M. F., Hizam, H., & Pouresmaeil, E. (2010). Distributed energy resources and benefits to the environment. Renewable and Sustainable Energy Reviews, 14(2), 724–734.

    Article  Google Scholar 

  274. Bloomquist, R. G. (2003). Geothermal space heating. Geothermics, 32(4), 513–526.

    Article  Google Scholar 

  275. Shen, L. Y., & Tam, V. W. (2002). Implementation of environmental management in the Hong Kong construction industry. International Journal of Project Management, 20(7), 535–543.

    Article  Google Scholar 

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

    Article  Google Scholar 

  277. Payraudeau, S., & van der Werf, H. M. (2005). Environmental impact assessment for a farming region: a review of methods. Agriculture, Ecosystems & Environment, 107(1), 1–19.

    Article  Google Scholar 

  278. Krajnc, D., & Glavič, P. (2005). A model for integrated assessment of sustainable development. Resources, Conservation and Recycling, 43(2), 189–208.

    Article  Google Scholar 

  279. Menegaki, A. (2008). Valuation for renewable energy: a comparative review. Renewable and Sustainable Energy Reviews, 12(9), 2422–2437.

    Article  Google Scholar 

  280. Tam, C. M., Tam, V. W., & Tsui, W. S. (2004). Green construction assessment for environmental management in the construction industry of Hong Kong. International Journal of Project Management, 22(7), 563–571.

    Article  Google Scholar 

  281. Berardi, U. (2013). Stakeholders’ influence on the adoption of energy-saving technologies in Italian homes. Energy Policy, 60, 520–530.

    Article  Google Scholar 

  282. Gilchrist, A., & Allouche, E. N. (2005). Quantification of social costs associated with construction projects: state-of-the-art review. Tunnelling and Underground Space Technology, 20(1), 89–104.

    Article  Google Scholar 

  283. Christie, N., Smyth, K., Barnes, R., & Elliott, M. (2014). Co-location of activities and designations: A means of solving or creating problems in marine spatial planning? Marine Policy, 43, 254–261.

    Article  Google Scholar 

  284. Geo-energy.org, Geothermal Basics – Basics. (2015). [Online]. Available: http://geo-energy.org/Basics.aspx. Accessed 25 June 2015.

  285. Huang, B., Ouyang, Z., Zheng, H., Zhang, H., & Wang, X. (2008). Construction of an eco-island: a case study of Chongming Island, China. Ocean and Coastal Management, 51(8), 575–588.

    Article  Google Scholar 

  286. Energyalmanac.ca.gov, Types of Geothermal Power Plants. (2015). [Online]. Available: http://energyalmanac.ca.gov/renewables/geothermal/types.html. Accessed 25 June 2015.

  287. Yari, M. (2010). Exergetic analysis of various types of geothermal power plants. Renewable Energy, 35(1), 112–121.

    Article  Google Scholar 

  288. Shanben, Q., Fangxun, L. I., & Nianru, L. I. (1989). Turbogenerator units operating on duo-flash cycle in the Yangbajain geothermal power plant, Tibet, China. InEnergy Conversion Engineering Conference, 1989. IECEC-89., Proceedings of the 24th Intersociety (pp. 2151–2154).

    Google Scholar 

  289. Gallup, D. L. (1996). Combination flash-bottoming cycle geothermal power generation: a case history. InEnergy conversion engineering conference, 1996. IECEC 96., proceedings of the 31st intersociety (Vol. 3, pp. 1622–1627).

    Chapter  Google Scholar 

  290. Jalilinasrabady, S., Itoi, R., Valdimarsson, P., Saevarsdottir, G., & Fujii, H. (2012). Flash cycle optimization of Sabalan geothermal power plant employing exergy concept. Geothermics, 43, 75–82.

    Article  Google Scholar 

  291. Luo, C., Huang, L., Gong, Y., & Ma, W. (2012). Thermodynamic comparison of different types of geothermal power plant systems and case studies in China. Renewable Energy, 48, 155–160.

    Article  Google Scholar 

  292. Geothermal.marin.org, Introduction to Geothermal Energy – Power Plant Geysers 2. (2015). [Online]. Available: http://geothermal.marin.org/geopresentation/sld053.htm. Accessed 25 June 2015.

  293. Onlinenevada.org, Steamboat Springs Geothermal Field | ONE. (2015). [Online]. Available: http://www.onlinenevada.org/articles/steamboat-springs-geothermal-field. Accessed 25 June 2015.

  294. Bliem, C. J., & Mines, G. L. (1991). Advanced binary geothermal power plants: Limits of performance. Idaho Falls: EG and G Idaho.

    Book  Google Scholar 

  295. Lund, J. W., & Klein, R. (1995). Prawn Park-Taupo, New Zealand. Geo-Heat Center Quarterly Bulletin, 16(4), 27–29.

    Google Scholar 

  296. Truesdell, A. H., & White, D. E. (1973). Production of superheated steam from vapor-dominated geothermal reservoirs. Geothermics, 2(3), 154–173.

    Article  Google Scholar 

  297. Kanoglu, M. (2002). Exergy analysis of a dual-level binary geothermal power plant. Geothermics, 31(6), 709–724.

    Article  Google Scholar 

  298. Kanoğlu, M., & Çengel, Y. A. (1999). Economic evaluation of geothermal power generation, heating, and cooling. Energy, 24(6), 501–509.

    Article  Google Scholar 

  299. Toni, B. (2011). Geothermal demonstration project.

    Google Scholar 

  300. Oit.edu, Geo-Heat Center. (2015). [Online]. Available: http://www.oit.edu/orec/geo-heat-center. Accessed 25 June 2015.

  301. Renewable Energy in Oregon, American Council On Renewable Energy (ACORE), September 2014.

    Google Scholar 

  302. Geothermal Exchange Organization, Geothermal Exchange Organization – Geothermal Heat Pumps. (2015). [Online]. Available: https://www.geoexchange.org/. Accessed 25 June 2015.

  303. Energy.gov, Direct Use of Geothermal Energy | Department of Energy. (2015). [Online]. Available: http://energy.gov/eere/geothermal/direct-use-geothermal-energy. Accessed 25 June 2015.

  304. Huttrer, G. W. (2001). The status of world geothermal power generation 1995–2000. Geothermics, 30(1), 1–27.

    Article  Google Scholar 

  305. Hurter, S., & Schellschmidt, R. (2003). Atlas of geothermal resources in Europe. Geothermics, 32(4), 779–787.

    Article  Google Scholar 

  306. Cataldi, R., Mongelli, F., Squarci, P., Taffi, L., Zito, G., & Calore, C. (1995). Geothermal ranking of Italian territory. Geothermics, 24(1), 115–129.

    Article  Google Scholar 

  307. Lund, J. W. (2003). The USA geothermal country update. Geothermics, 32(4), 409–418.

    Article  Google Scholar 

  308. Energy.gov, Geothermal Heat Pumps. (2015). [Online]. Available: http://energy.gov/energysaver/articles/geothermal-heat-pumps. Accessed 25 June 2015.

  309. Oregon.gov, Geothermal Energy Ground-Source Heat Pumps. (2015). [Online]. Available: http://www.oregon.gov/energy/renew/geothermal/pages/gshp.aspx. Accessed 25 June 2015.

  310. Current, J., Ratick, S., & ReVelle, C. (1998). Dynamic facility location when the total number of facilities is uncertain: A decision analysis approach. European Journal of Operational Research, 110(3), 597–609.

    Article  Google Scholar 

  311. Okoli, C., & Pawlowski, S. D. (2004). The Delphi method as a research tool: an example, design considerations and applications. Information Management, 42(1), 15–29.

    Article  Google Scholar 

  312. Sheikh, N., Daim, T., & Kocaoglu, D. F. (2011). Use of multiple perspectives and decision modeling for PV technology assessment. InTechnology Management in the Energy Smart World (PICMET 2011) (pp. 1–21).

    Google Scholar 

  313. Nutt, D. J., King, L. A., & Phillips, L. D. (2010). Drug harms in the UK: a multicriteria decision analysis. The Lancet, 376(9752), 1558–1565.

    Article  Google Scholar 

  314. Kim, S. Y., Karlawish, J. H., & Caine, E. D. (2002). Current state of research on decision-making competence of cognitively impaired elderly persons. The American Journal of Geriatric Psychiatry, 10(2), 151–165.

    Article  Google Scholar 

  315. Liberatore, M. J., & Nydick, R. L. (2008). The analytic hierarchy process in medical and health care decision making: A literature review. European Journal of Operational Research, 189(1), 194–207.

    Article  Google Scholar 

  316. Shanteau, J. (1988). Psychological characteristics and strategies of expert decision makers. Acta Psychologica, 68(1–3), 203–215.

    Article  Google Scholar 

  317. Daim, T. U. (1998). Technology evaluation and acquisition strategies and their implications in the US electronics manufacturing industry, Portland State University.

    Google Scholar 

  318. Fink, A. J. M. R. H. (1984). American Journal of Public Health, 74, 979–983.

    Article  Google Scholar 

  319. Weiss, D. J., & Shanteau, J. (2003). Empirical assessment of expertise. Human Factors: The Journal of the Human Factors and Ergonomics Society, 45(1), 104–116.

    Article  Google Scholar 

  320. Ra, J. W. (1988). Analysis of expert judgments in hierarchical decision process. University of Pittsburgh.

    Google Scholar 

  321. G. Council, What is Geothermal? – Geothermal Resources Council, Geothermal.org . (2017). [Online]. Available: https://geothermal.org/what.html. Accessed 19 Jan 2017.

  322. Geothermal heat pump, En.wikipedia.org. (2017). [Online]. Available: https://en.wikipedia.org/wiki/Geothermal_heat_pump. Accessed: 19 Jan 2017.

  323. Geothermal Today. (2003). 1st ed. U.S Department of Energy.

    Google Scholar 

  324. (2017). [Online]. Available: https://www.nrel.gov/workingwithus/re-geo-elec-production.html. Accessed 19 Jan 2017.

  325. Paired Comparison Analysis: Working Out Relative Importances, Mindtools.com . (2017). [Online]. Available: https://www.mindtools.com/pages/article/newTED_02.htm. Accessed 19 Jan 2017.

Download references

Author information

Authors and Affiliations

  1. Portland State University, Portland, OR, USA

    Ahmed Shehab Alshareef & Tugrul U. Daim

  2. Demandbase, Seattle, OR, USA

    Ibrahim Iskin

Authors
  1. Ahmed Shehab Alshareef
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tugrul U. Daim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ibrahim Iskin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tugrul U. Daim .

Editor information

Editors and Affiliations

  1. Department of Engineering and Technology Management, Portland State University, Portland, Oregon, USA

    Tugrul U. Daim

  2. School of Business, Pacific Lutheran University, Tacoma, Washington, USA

    Leong Chan

  3. Bonneville Power Administration, Portland, Oregon, USA

    Judith Estep

Appendices

Appendices

1.1 Appendix A: Instruments for the Invitation of Experts

1.1.1 Appendix A1: The Invitation of Experts for Participation in My M.S. Thesis Research

Dear ………………………,

My name is Ahmed Alshareef and I am an M.S. student from the Department of Engineering and Technology Management at Portland State University. I am writing to invite you to participate in my research study called “Technology Assessment Model of Developing Geothermal Energy Resources for Supporting Electrical System: The Case for Oregon.” This research study is being conducted in partial fulfillment of the requirements for a master’s degree in engineering and technology management at Portland State University.

You’re eligible to be in this study because you are an expert from either academia or industry and have enough experience to provide feedback on the criteria in the model I am researching.

Your participation in my research is important to developing a framework, measurement system, and metric for reaching the best benefit of geothermal energy resources. My research looks at the problem from different perspectives and dimensions with respect to utility objectives and goals.

The proposed research model that I developed requires participation of experts who have knowledge and opinions in the topic area of geothermal energy resources. Participation in the online survey/evaluation will take approximately 30 min to complete. This will help to further construct the model and establish a weight for selecting elements that require further development.

If you decide to participate in this study, you will make judgments on different criteria, using paired comparison between two elements, deciding which element is more important between the two. Remember, this is completely voluntary. You can choose to be in the study or not.

If you’d like to participate or have any questions about the study, please email or contact me at aalsha2@pdx.edu or call me at (503) 867–9279.

If you have any concerns or problems about participating in this study or your rights as a research subject, please contact the PSU Office of Research Integrity, 1600 SW 4th Ave., Market Center Building Ste. 620, Portland, OR, 97201 (phone (503) 725–2227 or 1 (877) 480–4400).

Thank you very much.

Sincerely,

Ahmed Alshareef

M.S. Student

Department of Engineering and Technology Management

Portland State University

1.1.2 Appendix A2: Informed Consent Form

1.1.2.1 Informed Consent Template: Online Survey Consent

You are invited to participate in a research study entitled “Technology Assessment Model of Developing Geothermal Energy Resources for Supporting Electrical System.” The study is being conducted by me, Ahmed Alshareef, a graduate student from the Department of Engineering and Technology Management at Portland State University. The study is under the supervision of my advisor, Tugrul Daim.

The purpose of this research study is to examine which technologies are important for developing geothermal energy. Your participation in the study will contribute to a better understanding of the different criteria with more knowledge to know which criteria in the model require developing and more research work to cover it from a different perspective. This project is being conducted in partial fulfillment of the requirements for an M.S. degree under the supervision of Dr. Tugrul U. Daim. You are invited as a potential participant due to your expertise in the area of energy sector due to your qualification and professional experience. You are free to contact the investigator at the above address and phone number to discuss the study. You must be at least 18 years old to participate.

If you agree to participate, the evaluation will take approximately 30 min of your time, and you will complete an activity about Developing Geothermal Energy Resources for Supporting Electrical System.

There are no known risks for participating and all the information will be kept in my laptop, and I will destroy the information after 1 year of graduation. There are no costs for participating, nor will you personally benefit from participating. Your name and email address will be collected during the data collection phase for tracking purposes only. Identifying information will be stripped from the final dataset.

Your participation in this study is voluntary. You may decline to answer any question and you have the right to withdraw from participation at any time. Withdrawal will not affect your relationship with Portland State University in any way. If you do not want to participate, either simply stop participating or close the browser window. I may send study reminders about participation in the study. If you do not want to receive any more reminders, you may email me at aalsha2@pdx.edu.

If you have any questions about the study or need to update your email address, contact me, Ahmed Alshareef, at 503–867-9279 or send an email to aalsha2@pdx.edu. You may also contact my advisor, Tugrul Daim, at ji2td@pdx.edu.

If you have questions about your rights or are dissatisfied at any time with any part of this study, you can contact the Human Subjects Research Review Committee at hsrrc@pdx.edu, Market Center Building, 6th Floor, 1600 SW 4th Ave., Portland, OR 97201.

If you agree to participate, click on the following link [HTTP://LINK TO STUDY URL].

Thank you.

Please print a copy of this document for your records.

1.1.3 Appendix A3: Content Web Survey

Dear ……..,

Thank you so much for accepting the invitation to complete the survey for my thesis research (Technology Assessment Model of Developing Geothermal Energy Resource for Supporting Electrical System). I have attached the link of the survey, the instructions, and the explanation of the research. You can see the details of each node in the model of the survey by pointing your cursor over the node. Each node had been explained in the instruction document.

Thank you very much.

Sincerely,

Ahmed Alshareef

M.S. Student

Department of Engineering and Technology Management

Portland State University

1.1.4 Appendix A4: Content Questionnaire Survey

figure a
figure b
figure c
figure d
figure e
figure f
figure g
figure h
figure i
figure j
figure k
figure l
figure m
figure n
figure o
figure p
figure q
figure r
figure s
figure t
figure u

1.1.5 Appendix A5: Content Instructions and Explanation of Nodes

The figure below shows the proposed research model. This figure will be used to establish the weight of each element and analyze the model.

figure v

Based on a comprehensive literature review and by validating the proposed research model with my advisor Dr. Tugrul U. Daim, this research model will be used for asking experts to establish their weighted output relative to geothermal energy resources. The data collection will be created from this research model to establish the final results of this study.

All the development of the proposed research model will stay in the same frame of the human subject research, and it will not change the HSRRC application. In addition, the goal and objective of the research will be kept from any change.

The objective of the proposed research is to develop the assessment model framework that can be used for supporting cost-effective renewable energy in Oregon by the development of geothermal energy sources. A mission-oriented model, hierarchical decision-making (HDM), will be used to determine the goal that represents the case for Oregon.

HDM is the approach that will be used for analyzing the research objective and criteria used to inform decision about how to inform geothermal energy since HDM works with complicated processes and looks at the problem from different perspectives. All the development occurred to the proposed research model works through criteria, sub-criteria, and alternatives, and this research model comes from a comprehensive review of literature.

The purpose of the data collection is to ensure the relative importance of decision elements through a numerical quantification process. Using the pairwise comparison method between two elements to evaluate distributional balance is necessary in order to know which element is more important than another. A pairwise comparison will use 100 points scale to make the balance. Defining each element will be clarified below.

  • Encourage community to support geothermal energy project: Using geothermal energy project will make future customer life easier and more convenient; the result of using these geothermal energy projects will encourage customers to support geothermal projects. Also, it will increase the adoption and development of geothermal energy.

  • Minimize environmental impact: Using geothermal energy will have a positive impact on the environment since it does not consume a huge amount of fuel.

  • Reduce expense of investment energy projects: Different technologies that accompany geothermal energy resources will change the expenses of investment if more attention and effort are given to this area of alternative energy.

  • Technical option improvement for geothermal energy projects: There is a possibility to develop the process in the future by quickly responding to any changes in the market and in the requirements of customers.

  • Minimize the negative impact on the general public: Reducing the negative impact on the general public and public spaces ensures that these geothermal projects do not interact with other projects in the same area.

  • Create new job opportunity: When geothermal energy resources are constructed, this construction will require a diversity of skills to complete.

  • Social acceptance: The continued commitment to expand and improve federal lands for the use of geothermal resources will lead to an increase in production.

  • GHG emissions: Due to lower GHG emissions, geothermal energy projects have less impact on the environment compared with other sources of energy.

  • Land requirement: Geothermal fields require fewer acres compared with other sources of energy.

  • Seismic activity: While the extraction of geothermal energy can lead to seismic activity, this event would most likely be less than magnitude of 2.5 on the Richter scale (earthquakes usually cannot be felt under 3.5).

  • Using the land for other purposes: When the activity of a power plant is completed, the land can be rehabilitated and used for livestock grazing or other agriculture purposes.

  • Minimize the capital cost: Projects of geothermal energy resources have the potential to reduce the cost of investments if the investments are made over a long period.

  • Minimize operation cost: Geothermal projects can increase the energy production and reduce the cost.

  • Economy boost: “Geothermal projects have the potential to enhance the economies through increased tax revenues, the creation of new businesses and local jobs, and enhanced community involvement” [321].

  • Minimizing the demand of critical resources: Geothermal projects reduce the demand on traditional resources like oil, coal, and natural gas.

  • Increasing the capacity of the energy system: Using geothermal energy resources will minimize the load on the electrical system and will simplify the challenges associated with increased energy load.

  • Equipment manufacturing development: In spite of the variety of geothermal energy equipment in the market, this equipment still needs more development to increase the geothermal energy efficiency, and for that technologies will need to be developed to be used in the manufacturing of this equipment.

  • Minimize noise and odor: It is important for geothermal energy projects to work without negatively impacting the general public by avoiding and reducing noise and odor as quickly as possible.

  • Minimizing property damage for reducing impact on lifestyle: It is important for geothermal energy projects to minimize the routes to and from the site to avoid any conflicts or obstacles to the movement of residential and commercial activities.

  • Geothermal heat pump: “Is a central heating and/or cooling system that transfers heat to or from the ground. Geothermal heat pumps use the natural insulating properties of the earth from just a few feet underground to as much as several 100 feet deep, offering a unique and highly efficient renewable energy technology for heating and cooling” [322].

  • Direct use of geothermal heat: “refers to the immediate use of the energy for both heating and cooling applications. It is the use of underground hot water to heat buildings, …and for many other applications. District heating applications use networks of piped hot water to heat buildings in whole communities” [323].

  • Geothermal electricity: “Geothermal power plants use steam produced from reservoirs of hot water found a few miles or more below the Earth’s surface to produce electricity. The extremely high temperatures in the deeper geothermal reservoirs are used for the generation of electricity. The steam rotates a turbine that activates a generator, which produces electricity” [324].

figure w

To assess geothermal energy resources for supporting electrical system

The mission of this model is to assess geothermal energy resources for supporting the electrical system. This process will require weighting objectives, criteria, and alternatives. A pairwise comparison is required for this purpose to rate criteria (objectives) with respect to each other. As the model is built based on HDM, “a pairwise comparison helps you work out the importance of a number of options relative to one another. This makes it easy to choose the most important problem to solve, or to pick the solution that will be most effective. It also helps you set priorities where there are conflicting demands on your resources. The tool is particularly useful when you don’t have objective data to use to make your decision” [325]. This process for technology assessment model of developing geothermal energy resources requires having a scale with 100 points distributed between these main criteria. The criteria with high points result from experts choosing this criterion, while the criteria with low points result from few experts choosing this criterion. Also, the score of 0 will not be valid and the score for this situation must be at least 1 point.

This is an example of how to weight, evaluate, and compare:

Considering two objectives, “Objective A” and “Objective “B,” choose the point value that you think is necessary. Since the system is based on 100 points, this can be weighted as A = 55 and B = 45.

  1. 1.1

    100 points must be distributed between the following pairs of geothermal energy objectives to reflect your judgment on their relative importance to the overall goal for this study.

The importance of encouraging community to support geothermal energy to minimize the environment impact:

figure x
  1. 1.2

    100 points must be distributed between the following pairs of geothermal energy objectives to reflect your judgment on their relative importance to the overall goal for this study.

The importance of encouraging community to support geothermal energy to reduce expense of investment energy project:

figure y
  1. 1.3

    100 points must be distributed between the following pairs of geothermal energy objectives to reflect your judgment on their relative importance to the overall goal for this study.

The importance of encouraging community to support geothermal energy to technical option improvement for geothermal energy project:

figure z
  1. 1.4

    100 points must be distributed between the following pairs of geothermal energy objectives to reflect your judgment on their relative importance to the overall goal for this study.

The importance of encouraging community to support geothermal energy to minimize the negative impact on general public:

figure aa
  1. 1.5

    100 points must be distributed between the following pairs of geothermal energy objectives to reflect your judgment on their relative importance to the overall goal for this study.

The importance of minimizing the environment impact to reduce expense of investment energy project:

figure ab
  1. 1.6

    100 points must be distributed between the following pairs of geothermal energy objectives to reflect your judgment on their relative importance to the overall goal for this study.

The importance of minimizing the environment impact to technical option improvement for geothermal energy project:

figure ac
  1. 1.7

    100 points must be distributed between the following pairs of geothermal energy objectives to reflect your judgment on their relative importance to the overall goal for this study.

The importance of minimizing the environment impact to minimize the negative impact on general public:

figure ad
  1. 1.8

    100 points must be distributed between the following pairs of geothermal energy objectives to reflect your judgment on their relative importance to the overall goal for this study.

The importance of reducing expense of investment energy project to technical option improvement for geothermal energy project:

figure ae
  1. 1.9

    100 points must be distributed between the following pairs of geothermal energy objectives to reflect your judgment on their relative importance to the overall goal for this study.

The importance of reducing expense of investment energy project to minimize the negative impact on general public:

figure af
  1. 1.10

    100 points must be distributed between the following pairs of geothermal energy objectives to reflect your judgment on their relative importance to the overall goal for this study.

The importance of technical option improvement for geothermal energy project to minimize the negative impact on general public:

figure ag

1.2 Appendix B: Judgment Quantifications

1.2.1 Appendix B1: Judgment Quantification for the Objectives Level with Respect to the Mission

The table below shows the ratio of judgment quantification, as explained in the example: using CS 70 and IC 30, this will be written as CS/IC = 70, and 30 will not appear in the table.

Encourage community to support geothermal energy projects, CS

Minimize environmental impact, EI

Minimize investment cost, IC

Technical option improvement for geothermal energy projects, TI

Minimize the negative impact on the general public, NI

 

CS/EI

CS/IC

CS/TI

CS/NI

EI/IC

EI/TI

EI/NI

IC/TI

IC/NI

TI/NI

Expert 1

50

50

50

50

40

40

60

50

75

75

Expert 2

42

42

18

42

46

50

44

35

60

63

Expert 3

10

55

75

40

80

75

80

20

20

49

Expert 4

18

21

58

50

71

74

86

65

39

60

Expert 5

50

50

70

70

20

20

70

50

70

80

Expert 6

59

50

32

51

41

62

34

89

52

18

Expert 7

70

35

80

45

50

75

35

40

45

20

1.2.2 Appendix B2: Judgment Quantification for the Goals Level with Respect to Objectives

The table below shows the ratio of judgment quantification, as explained in the example: using JO 70 and SC 30, this will be written as JO/SC = 70, and 30 will not appear in the table.

Encourage Community to Support Geothermal Energy Projects

  • Create new job opportunity: JO

  • Social acceptance: SC

 

JO/SC

Expert 1

60

Expert 2

40

Expert 3

70

Expert 4

12

Expert 5

90

Expert 6

67

Expert 7

75

Minimize Environmental Impact

  • GHG emission: GE

  • Land requirement: LR

  • Seismic activity: SA

  • Using the land for other purposes: UL

 

GE/LR

GE/SA

GE/UL

LR/SA

LR/UL

SA/UL

Expert 1

50

75

75

75

75

50

Expert 2

72

55

78

50

32

63

Expert 3

99

50

90

10

40

50

Expert 4

70

10

75

10

45

80

Expert 5

50

80

60

89

20

10

Expert 6

43

22

16

29

50

71

Expert 7

5

30

85

15

50

85

Reduce Expense of Investment Energy Projects

  • Minimize capital cost: CC

  • Minimize operation cost: OC

  • Economic boost: EB

 

CC/OC

CC/EB

OC/EB

Expert 1

50

80

80

Expert 2

58

84

66

Expert 3

70

25

20

Expert 4

80

95

95

Expert 5

95

90

70

Expert 6

50

50

50

Expert 7

65

60

75

Technical Option Improvement for Geothermal Energy Projects

  • Minimizing the demand of critical resources: CR

  • Increasing the capacity of energy system: CS

  • Equipment manufacturing development: ED

 

CR/CS

CR/ED

CS/ED

Expert 1

40

50

60

Expert 2

35

15

31

Expert 3

90

70

20

Expert 4

50

80

95

Expert 5

25

50

70

Expert 6

31

38

66

Expert 7

25

30

25

Minimize the Negative Impact on the General Public

  • Minimize noise and odor: NO

  • Minimizing property damage for reducing impact on lifestyle: PD

 

NO/PD

Expert 1

50

Expert 2

66

Expert 3

80

Expert 4

75

Expert 5

99

Expert 6

50

Expert 7

90

1.2.3 Appendix B3: Judgment Quantification for the Alternatives Level with Respect to Goals

The table below shows the ratio of judgment quantification, as explained in the example: using GE 70 and DH 30, this will be written as GE/DH=70, and 30 will not appear in the table.

  • Geothermal electricity: GE

  • Direct use of geothermal heat: DH

  • Geothermal heat pump: GH

Alternatives: create new job opportunity

 

GH/DH

GH/GE

DH/GE

Expert 1

60

40

25

Expert 2

29

71

91

Expert 3

65

40

39

Expert 4

65

35

30

Expert 5

90

10

5

Expert 6

50

50

50

Expert 7

20

15

50

Alternatives: social acceptance

 

GH/DH

GH/GE

DH/GE

Expert 1

60

60

50

Expert 2

30

48

77

Expert 3

30

40

56

Expert 4

60

50

20

Expert 5

50

20

10

Expert 6

50

50

50

Expert 7

50

50

50

Alternatives: GHG emission

 

GH/DH

GH/GE

DH/GE

Expert 1

50

35

35

Expert 2

42

67

68

Expert 3

25

85

70

Expert 4

70

50

30

Expert 5

80

10

1

Expert 6

50

50

50

Expert 7

50

50

50

Alternatives: land requirement

 

GH/DH

GH/GE

DH/GE

Expert 1

50

50

50

Expert 2

53

65

51

Expert 3

50

25

40

Expert 4

70

50

70

Expert 5

80

13

5

Expert 6

50

50

50

Expert 7

10

10

10

Alternatives: seismic activity

 

GH/DH

GH/GE

DH/GE

Expert 1

75

90

75

Expert 2

45

62

69

Expert 3

49

50

50

Expert 4

50

50

50

Expert 5

50

50

50

Expert 6

50

50

50

Expert 7

5

5

50

Alternatives: using the land for other purposes

 

GH/DH

GH/GE

DH/GE

Expert 1

50

50

50

Expert 2

50

35

35

Expert 3

60

70

60

Expert 4

75

50

20

Expert 5

50

20

10

Expert 6

50

50

50

Expert 7

10

5

50

Alternatives: minimize capital cost

 

GH/DH

GH/GE

DH/GE

Expert 1

40

40

50

Expert 2

22

22

72

Expert 3

60

30

20

Expert 4

40

70

70

Expert 5

50

20

10

Expert 6

50

50

50

Expert 7

30

30

35

Alternatives: minimize operation cost

 

GH/DH

GH/GE

DH/GE

Expert 1

50

50

50

Expert 2

37

26

54

Expert 3

61

25

20

Expert 4

30

50

60

Expert 5

75

10

5

Expert 6

50

50

50

Expert 7

35

20

30

Alternatives: economic boost

 

GH/DH

GH/GE

DH/GE

Expert 1

50

25

25

Expert 2

30

33

64

Expert 3

65

60

60

Expert 4

65

50

30

Expert 5

50

40

30

Expert 6

50

50

50

Expert 7

10

5

40

Alternatives: minimizing the demand of critical resources

 

GH/DH

GH/GE

DH/GE

Expert 1

50

50

50

Expert 2

50

67

68

Expert 3

30

60

80

Expert 4

50

15

15

Expert 5

50

40

25

Expert 6

50

50

50

Expert 7

30

30

50

Alternatives: increasing the capacity of the energy system

 

GH/DH

GH/GE

DH/GE

Expert 1

40

20

25

Expert 2

50

63

63

Expert 3

50

30

15

Expert 4

50

30

20

Expert 5

80

1

1

Expert 6

50

50

50

Expert 7

50

50

50

Alternatives: equipment manufacturing development

 

GH/DH

GH/GE

DH/GE

Expert 1

25

10

25

Expert 2

26

28

54

Expert 3

60

40

30

Expert 4

70

50

35

Expert 5

70

30

1

Expert 6

50

50

50

Expert 7

50

5

15

Alternatives: minimize noise and odor

 

GH/DH

GH/GE

DH/GE

Expert 1

50

10

10

Expert 2

43

65

76

Expert 3

55

30

30

Expert 4

50

50

50

Expert 5

70

20

11

Expert 6

50

50

50

Expert 7

50

50

50

Alternatives: minimizing property damage for reducing impact on lifestyle

 

GH/DH

GH/GE

DH/GE

Expert 1

25

25

50

Expert 2

50

50

50

Expert 3

50

25

20

Expert 4

50

50

50

Expert 5

50

20

10

Expert 6

50

50

50

Expert 7

30

20

40

1.3 Appendix C: Calculations (Overall Weight)

Objectives

Goals

GHP

Direct heat

Geothermal electricity

 

Local

Global

Local

Global

Local

Global

Local

Global

Encourage community to support geothermal energy project

0.17

Create new job opportunity

0.59

0.1

0.25

0.025

0.3

0.04

0.45

0.045

 

Social acceptance

0.41

0.07

0.29

0.02

0.32

0.022

0.39

0.027

Minimize environmental impact

0.26

GHG emission

0.3

0.078

0.3

0.023

0.3

0.023

0.4

0.03

 

Land requirement

0.2

0.053

0.28

0.014

0.3

0.016

0.42

0.02

 

Seismic activity

0.32

0.083

0.34

0.028

0.36

0.03

0.3

0.024

 

Using the land for other purposes

0.18

0.046

0.29

0.013

0.27

0.012

0.44

0.02

Reduce expense of investment energy projects

0.21

Minimize capital cost

0.51

0.107

0.23

0.024

0.33

0.036

0.44

0.047

 

Minimize operation cost

0 28

0.059

0.22

0.013

0.29

0.018

0.49

0.028

 

Economic boost

0.21

0.044

0.27

0.012

0.3

0.014

0.43

0.018

Technical option improvement for geothermal energy projects

0.18

Minimizing the demand of critical resources

0.28

0.05

0.27

0.013

0.35

0.018

0.38

0.019

 

Increasing the capacity of the energy system

0.39

0.07

0.24

0.016

0.23

0.017

0.53

0.037

 

Equipment manufacturing development

0.33

0.06

0.22

0.013

0.22

0.014

0.56

0.033

Minimize the negative impact on the general public

0.18

Minimize noise and odor

0.73

0.131

0.27

0.035

0.27

0.036

0.46

0.06

 

Minimizing property damage for reducing impact on lifestyle

0.27

0.049

0.23

0.011

0.29

0.014

0.48

0.022

 

0.26

 

0.31

 

0.43

1.4 Appendix D: Objectives Weight for Different Characteristics of Experts

Objectives weight for different characteristics of experts: background of organization

Objectives and goals

Utility

Consulting

Research lab

University

Encourage community to support geothermal energy project

0.15

0.16

0.25

0.16

Create new job opportunity

0.43

0.65

0.9

0.67

Social acceptance

0.57

0.35

0.1

0.33

Minimize environmental impact

0.28

0.34

0.12

0.16

GHG emission

0.23

0.5

0.31

0.1

Land requirement

0.2

0.2

0.23

0.18

Seismic activity

0.46

0.19

0.05

0.46

Using the land for other purposes

0.11

0.11

0.42

0.26

Reduce expense of investment energy projects

0.19

0.17

0.28

0.31

Minimize capital cost

0.56

0.35

0.86

0.33

Minimize operation cost

0.33

0.29

0.08

0.33

Economic boost

0.11

0.36

0.06

0.33

Technical option improvement for geothermal energy projects

0.18

0.19

0.26

0.1

Minimizing the demand of critical resources

0.21

0.47

0.21

0.2

Increasing the capacity of the energy system

0.38

0.25

0.57

0.51

Equipment manufacturing development

0.41

0.28

0.22

0.29

Minimize the negative impact on the general public

0.2

0.14

0.08

0.27

Minimize noise and odor

0.77

0.65

0.99

0.5

Minimizing property damage for reducing impact on lifestyle

0.23

0.35

0.01

0.5

Objectives weight for different characteristics of experts: background of organization

 

Alternatives

Utility

Consulting

Research lab

University

GHP

Direct heat

Geo. elect.

GHP

Direct heat

Geo. elect.

GHP

Direct heat

Geo. elect.

GHP

Direct heat

Geo. elect.

Goals

Create new job opportunity

0.21

0.43

0.35

0.33

0.21

0.47

0.15

0.03

0.82

0.33

0.33

0.33

Social acceptance

0.31

0.36

0.33

0.32

0.375

0.315

0.14

0.11

0.75

0.33

0.33

0.33

GHG emission

0.36

0.32

0.31

0.305

0.395

0.3

0.07

0.01

0.91

0.33

0.33

0.33

Land requirement

0.3

0.37

0.32

0.275

0.3

0.42

0.14

0.04

0.82

0.33

0.33

0.33

Seismic activity

0.23

0.42

0.34

0.51

0.285

0.205

0.33

0.33

0.33

0.33

0.33

0.33

Using the land for other purposes

0.24

0.27

0.49

0.405

0.32

0.27

0.14

0.11

0.75

0.33

0.33

0.33

Minimize capital cost

0.21

0.45

0.33

0.25

0.27

0.49

0.14

0.11

0.75

0.33

0.33

0.33

Minimize operation cost

0.19

0.37

0.43

0.275

0.24

0.48

0.11

0.04

0.85

0.33

0.33

0.33

Economic boost

0.2

0.34

0.44

0.325

0.25

0.425

0.28

0.24

0.48

0.33

0.33

0.33

Minimizing the demand of critical resources

0.23

0.31

0.44

0.29

0.465

0.245

0.27

0.22

0.51

0.33

0.33

0.33

Increasing the capacity of the energy system

0.31

0.3

0.38

0.18

0.185

0.635

0.02

0.01

0.98

0.33

0.33

0.33

Equipment manufacturing development

0.21

0.24

0.53

0.195

0.22

0.585

0.18

0.03

0.79

0.33

0.33

0.33

Minimize noise and odor

0.33

0.38

0.27

0.17

0.155

0.68

0.19

0.09

0.72

0.33

0.33

0.33

Minimizing property damage for reducing impact on lifestyle

0.26

0.33

0.39

0.165

0.3

0.53

0.14

0.11

0.75

0.33

0.33

0.33

Objectives weight for different characteristics of experts: position

Objectives, and goals

Management

Planning

Policy

Environment

Encourage community to support geothermal energy project

0.18

0.135

0.25

0.13

Create new job opportunity

0.49

0.53

0.9

0.7

Social acceptance

0.51

0.47

0.1

0.3

Minimize environmental impact

0.28

0.175

0.12

0.5

GHG emission

0.22

0.26

0.31

0.61

Land requirement

0.26

0.2

0.23

0.03

Seismic activity

0.41

0.35

0.05

0.26

Using the land for other purposes

0.11

0.19

0.42

0.1

Reduce expense of investment energy projects

0.21

0.255

0.28

0.08

Minimize capital cost

0.53

0.44

0.86

0.25

Minimize operation cost

0.36

0.33

0.08

0.13

Economic boost

0.11

0.23

0.06

0.62

Technical option improvement for geothermal energy projects

0.16

0.21

0.26

0.12

Minimizing the demand of critical resources

0.27

0.16

0.21

0.65

Increasing the capacity of the energy system

0.44

0.38

0.57

0.07

Equipment manufacturing development

0.29

0.46

0.22

0.28

Minimize the negative impact on the general public

0.17

0.225

0.08

0.17

Minimize noise and odor

0.72

0.58

0.99

0.8

Minimizing property damage for reducing impact on lifestyle

0.28

0.42

0.01

0.2

Objectives weight for different characteristics of experts: position

 

Alternatives

Management

Planning

Policy

Environment

 

GHP

Direct Heat

Geo. Elect

GHP

Direct Heat

Geo. Elect.

GHP

Direct Heat

Geo. Elect.

GHP

Direct Heat

Geo. Elect.

Goals

Create new job opportunity

0.23

0.27

0.49

0.28

0.505

0.205

0.15

0.03

0.82

0.35

0.23

0.43

Social acceptance

0.37

0.26

0.36

0.275

0.455

0.265

0.14

0.11

0.75

0.21

0.46

0.34

GHG emission

0.33

0.25

0.4

0.345

0.39

0.26

0.07

0.01

0.91

0.35

0.53

0.12

Land requirement

0.27

0.37

0.35

0.375

0.325

0.295

0.14

0.04

0.82

0.22

0.27

0.51

Seismic activity

0.35

0.35

0.3

0.34

0.385

0.27

0.33

0.33

0.33

0.33

0.34

0.33

Using the land for other purposes

0.26

0.29

0.44

0.295

0.295

0.405

0.14

0.11

0.75

0.48

0.31

0.21

Minimize capital cost

0.26

0.39

0.35

0.225

0.45

0.32

0.14

0.11

0.75

0.25

0.16

0.6

Minimize operation cost

0.23

0.35

0.4

0.26

0.36

0.375

0.11

0.04

0.85

0.22

0.15

0.63

Economic boost

0.21

0.25

0.54

0.255

0.41

0.325

0.28

0.24

0.48

0.45

0.3

0.25

Minimizing the demand of critical resources

0.21

0.29

0.49

0.365

0.37

0.26

0.27

0.22

0.51

0.25

0.6

0.16

Increasing the capacity of the energy system

0.23

0.24

0.52

0.36

0.36

0.28

0.02

0.01

0.98

0.21

0.15

0.64

Equipment manufacturing development

0.19

0.17

0.63

0.245

0.39

0.36

0.18

0.03

0.79

0.31

0.21

0.48

Minimize noise and odor

0.25

0.25

0.49

0.335

0.41

0.25

0.19

0.09

0.72

0.25

0.22

0.54

Minimizing property damage for reducing impact on lifestyle

0.2

0.36

0.43

0.33

0.33

0.33

0.14

0.11

0.75

0.19

0.17

0.63

Objectives weight for different characteristics of experts: education

Objectives and goals

Bachelor’s degree

Master’s degree

Ph.D. degree

Encourage community to support geothermal energy project

0.1

0.17

0.21

Create new job opportunity

0.12

0.61

0.78

Social acceptance

0.88

0.39

0.22

Minimize environmental impact

0.47

0.26

0.14

GHG emission

0.15

0.38

0.2

Land requirement

0.07

0.23

0.2

Seismic activity

0.68

0.27

0.26

Using the land for other purposes

0.09

0.12

0.34

Reduce expense of investment energy projects

0.18

0.18

0.3

Minimize capital cost

0.7

0.42

0.6

Minimize operation cost

0.28

0.32

0.21

Economic boost

0.02

0.26

0.19

Technical option improvement for geothermal energy projects

0.12

0.2

0.18

Minimizing the demand of critical resources

0.36

0.3

0.2

Increasing the capacity of the energy system

0.59

0.26

0.54

Equipment manufacturing development

0.05

0.44

0.26

Minimize the negative impact on the general public

0.12

0.19

0.17

Minimize noise and odor

0.75

0.72

0.75

Minimizing property damage for reducing impact on lifestyle

0.25

0.28

0.25

Objectives weight for different characteristics of experts: education

 

Alternatives

Bachelor’s degree

Master’s degree

Ph.D degree

 

GHP

Direct heat

Geo. elect.

GHP

Direct heat

Geo. Elect.

GHP

Direct heat

Geo. elect.

Goals

Create new job opportunity

0.31

0.19

0.5

0.245

0.3825

0.375

0.24

0.18

0.575

Social acceptance

0.35

0.17

0.48

0.2975

0.415

0.29

0.235

0.22

0.54

GHG emission

0.41

0.18

0.41

0.325

0.3925

0.28

0.2

0.17

0.62

Land requirement

0.43

0.33

0.25

0.255

0.3475

0.3925

0.235

0.185

0.575

Seismic activity

0.33

0.33

0.33

0.35

0.375

0.2775

0.33

0.33

0.33

Using the land for other purposes

0.42

0.13

0.46

0.2775

0.33

0.39

0.235

0.22

0.54

Minimize capital cost

0.36

0.47

0.18

0.1975

0.36

0.4475

0.235

0.22

0.54

Minimize operation cost

0.24

0.48

0.28

0.22

0.2825

0.4925

0.22

0.185

0.59

Economic boost

0.39

0.19

0.42

0.2175

0.3375

0.4425

0.305

0.285

0.405

Minimizing the demand of critical resources

0.13

0.13

0.74

0.29

0.4375

0.2725

0.3

0.275

0.42

Increasing the capacity of the energy system

0.22

0.18

0.6

0.27

0.2725

0.4575

0.175

0.17

0.655

Equipment manufacturing development

0.42

0.19

0.39

0.155

0.2475

0.5975

0.255

0.18

0.56

Minimize noise and odor

0.33

0.33

0.33

0.2525

0.2825

0.465

0.26

0.21

0.525

Minimizing property damage for reducing impact on lifestyle

0.33

0.33

0.33

0.2

0.3175

0.48

0.235

0.22

0.54

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Alshareef, A.S., Daim, T.U., Iskin, I. (2018). Technology Assessment: Developing Geothermal Energy Resources for Supporting Electrical System in Oregon. In: Daim, T., Chan, L., Estep, J. (eds) Infrastructure and Technology Management. Innovation, Technology, and Knowledge Management. Springer, Cham. https://doi.org/10.1007/978-3-319-68987-6_4

Download citation

Publish with us

Policies and ethics

Search

Navigation