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Development of Indicators for the National GHG Reduction Technology Selection Based on Delphi Method

델파이 기법을 활용한 국가 온실가스 감축기술 선택 지표 연구

  • Kim, Kiman (Division of Global Strategy, Green Technology Center) ;
  • Kang, Moon Jung (Division of Global Strategy, Green Technology Center) ;
  • Kim, Hyung-ju (Division of Policy Research, Green Technology Center)
  • 김기만 (녹색기술센터 국제전략부) ;
  • 강문정 (녹색기술센터 국제전략부) ;
  • 김형주 (녹색기술센터 정책연구부)
  • Received : 2018.07.17
  • Accepted : 2018.10.20
  • Published : 2018.10.28

Abstract

A strategic technology selection for GHG reduction is crucial to secure mitigation means. Especially, a technology selection for a public sector is encouraged to consider integrated perspectives due to various stakeholders under public goals. However, previous studies have mainly focused on technological and economic factors, moreover, consistent criteria have not been applied. This study develops indicators for the GHG reduction technology selection from the public perspective based on delphi method with 22 experts. The result provides valid indicators of technology selection for GHG reduction considering an aspect of technology, economics, environment, policy, society. Specifically, 16 indicators from 5 categories on commercialized technology, and 18 indicators from 5 categories on new technology. We expect that those indicators are useful for a decision-making tool of technology selection. Moreover, provide the basis for the study of judgement criteria to evaluate GHG reduction technology.

Keywords

National GHG Reduction;Reduction Implementation;Public Technology Selection;Delphi;Technology Selection Criteria

Acknowledgement

Supported by : Green Technology Center

References

  1. N. Dalkey, B. Brown & Cochran. (1970). Use of Self-ratings to Improve Group Estimates : Experimental Evaluation of Delphi Procedures. Technological Forecasting, 1, 283-291.
  2. J. W. Murry & J. O. Hammons. (1995). Delphi: A versatile methodology for conducting qualitative research. The Review of Higher Education, 18(4), 423-436.
  3. D. I. Kim, Y. Chung, Y. H. Lee. (2013). Delphi Study on Concepts and Components of Smart Media Addiction, Asian Journal of Education 14(4), 49-71.
  4. S. Y. Hwang. (2009). The development of e-CRM activities scale in tourism industry using a Delphi Method. Journal of Tourism Sciences, 33(5), 453-475.
  5. M. S. Lin, J. H. Park & S. B. Ahn, (2009). A Study on Selection of and Priority on Assessment Indicators in Green Logistics : Focused on Ports and Inland Hub Terminals. Journal of Korea Port Economic Association, 25(4), 1-20.
  6. E. A. Voudrias. (2016). Technology selection for infectious medical waste treatment using the analytic hierarchy process. Journal of the Air & Waste Management Association, 66(7), 663-672.
  7. J. J. Park & S. J. Yoon. (2011). A Comparison of stakeholders' view on the Priority of policy for Greenhouse Gas Reduction in the Power Sector. Korean Society and Pubic Administration, 22(3), 209-237.
  8. M. E. Baysal, A. Sarucan, C. Kahraman & O. Engin. (2011, July). The selection of renewable energy power plant technology using fuzzy data envelopment analysis. Proceedings of the World Congress on Engineering (pp. 1140-1143). London.
  9. S. G. Lee, G. Mogi & J. W. Kim. (2008), Multi-criteria decision making method for developing greenhouse gas technologies strategically considering scale efficiency: AHP/DEA CCR-I and BCC-I integrated model approach. Trans. Of the Korean Hydrogen and new energy society, 19(6), 552-560.
  10. J. M. Hong. (2011) An AHP Approach for The Importance Weight of Renewable Energy Investment Criterion in the Private Sector. Korean Energy Review, 10(1), 115-142.
  11. H. G. Cho, J. D. Kim, Y. D. Lee, Y. S. Shin & G. H. Kim. Assessment Model on Building Sustainability Using Multi-Criteria Decision Making Methodology, Korea science & art forum, 19, 635-645.
  12. Z. A. Muis, H. Hashim, Z. A. Manan, F. M. Taha & P. L. Douglas. (2010). Optimal planning of renewable energy-integrated electricity generation schemes with $CO_2$ reduction target. Renewable energy, 35(11), 2562-2570. https://doi.org/10.1016/j.renene.2010.03.032
  13. J. M. English & G. L. Kernan. (1976). The prediction of air travel and aircraft technology to the year 2000 using the Delphi method. Transportation Research, 10(1), 1-8.
  14. A. Heiko. (2012). Consensus measurement in Delphi studies: review and implications for future quality assurance. Technological forecasting and social change, 79(8), 1525-1536. https://doi.org/10.1016/j.techfore.2012.04.013
  15. Y. R. Kim. & Y. H. Choi. (2014). Korean Public Organization ERP Education Training Strategies Using Success Factor Analysis. Journal of the Korea Industrial Information Systems Research, 19(1), 87-97. https://doi.org/10.9723/JKSIIS.2014.19.1.087
  16. H. J. Kim & C. K. Park. (2012). A Study on the Evaluation Criteria for the Performance of Smart Grid Pilot Projects. Journal of Digital Convergence, 10(8), 15-20. https://doi.org/10.14400/JDPM.2012.10.8.015
  17. S. W. Bae. (2014). Study on the Development to the Competencies Model of Sport-for-all Instructors. Korean Journal of Sport Management, 19(5), 149-165.
  18. E. Im, K. C. Son & J. K. Kam. (2012). Development of Elements of Horticultural Therapy Evaluation Indices through Delphi Method. Korean Journal of Horticultural Science and Technology, 30(3), 308-324.
  19. G. Rowe & G. Wright. (1999). The Delphi technique as a forecasting tool: issues and analysis. International Journal of Forecasting, 15(4), 353-375. https://doi.org/10.1016/S0169-2070(99)00018-7
  20. D. R. Tojib & L. F. Sugianto. (2006). Content validity of instruments in IS research. Journal of Information Technology Theory and Application, 8(3), 5.
  21. V. Zamanzadeh, M. Rassouli, A. Abbaszadeh, H. A. Majd, A. Nikanfar & A. Ghahramanian. (2015). Details of content validity and objectifying it in instrument development. Nursing Practice Today, 1(3), 163-171.
  22. C. H. Lawshe. (1975). A quantitative approach to content validity. Personnel psychology, 28(4), 563-575. https://doi.org/10.1111/j.1744-6570.1975.tb01393.x
  23. J. Baker, K. Lovell & N. Harris. (2006). How expert are the experts? An exploration of the concept of'expert' within Delphi panel techniques. Nurse Researcher, 14(1), 59-70. https://doi.org/10.7748/nr2006.10.14.1.59.c6010
  24. A. Habibi, A. Sarafrazi & S. Izadyar. (2014). Delphi technique theoretical framework in qualitative research. The International Journal of Engineering and Science, 3(4), 8-13.
  25. C. Okoli & S. D. Pawlowski. (2004). The Delphi method as a research tool: an example, design considerations and applications. Information & management, 42(1), 15-29. https://doi.org/10.1016/j.im.2003.11.002
  26. D. K. Lee, S. J. Choi, S. U. Park, Y. J. Ha & J. T. Lee. (2004). An analysis on the CO2 Reduction and Sequestration Technology Using the AHP. Energy Engineering Journal, 13(3), 219-227.
  27. S. J. Ha & S. J. Kang. (2008). The Fuzzy AHP Approach to Prioritize the Future Energy Technology Development. Trans. of the Korean Hydrogen and New Energy Society, 19(5), 453-459.
  28. W. K. Park, G. Y. Kim, S. Lee & S. H. Lee. (2015). Investigating Multi-Attributes for GHGs Mitigation Technology in Agricultural Sectors : Applying the Analytic Hierarchy Process. Korean Journal of Agricultural Management and Policy, 42(3), 616-629.
  29. N. Oh & H. Kim. (2010). Analysis on Deduction of Energy-IT Convergence Technologies by the Analytic Hierarchy Process. The Journal of The Korean Institute of Communication Sciences 35(7), 1091-1097.
  30. M. S. Choi. (2007). An Evaluation of the Priority Order in Developing the Construction Technologies for Environment-friendly Apartment Houses-Laying Stress on the Viewpoints of Contractors -. Journal of the Architectural Institute of Korea Structure & Construction, 23(9), 213-220.
  31. C. Wimmler, G. Hejazi, E. de Oliveira Fernandes, C. Moreira & S. Connors. (2015). Multi-criteria decision support methods for renewable energy systems on islands. Journal of Clean Energy Technologies, 3(3), 185-195.
  32. S. H. Lee & J. H. Park. (2011). An Evaluation of Multi-Criteria for the Expansion of New and Renewable Energy in the Agricultural Sector. Korean Journal of Agricultural Science, 38(1), 183-190.
  33. F. Woudenberg. (1991). An evaluation of Delphi. Technological forecasting and social change, 40(2), 131-150. https://doi.org/10.1016/0040-1625(91)90002-W
  34. J. S. Lee. (2001). The Delphi Method. Seoul : KYOYOOKBOOK.
  35. M. Adler & E. Ziglio. (1996). Gazing into the oracle: the Delphi method and its application to social policy and public health. London and Philadelphia : Jessica Kingsley Publishers.
  36. S. Y. Roh. (2006). Delphi technique: forecasting based on professional intuition. Planning and Policy, 53-62.
  37. H. J. Choi, & C. J. Suh. (2011). Study on R&D Manpower Requirements for the Field of Pharmaceutical - An Application of Delphi Method. Korea Academy Industrial Cooperation Society, 12(3), 1270-1277.
  38. F. T. S. Chan, M. H. Chan & N. K. H. Tang. (2000). Evaluation methodologies for technology selection. Journal of Materials Processing Technology, 107(1), 330-337.
  39. P. Dussauge, S. Hart & B. Ramanantsoa. (1992). Strategic technology management. Wiley.
  40. Y. C. Shen, S. H. Chang, G. T. Lin & H. C. Yu. (2010). A hybrid selection model for emerging technology. Technological Forecasting and Social Change, 77(1), 151-166. https://doi.org/10.1016/j.techfore.2009.05.001
  41. M. Lamb & M. Gregory. (1997). Industrial concerns in technology selection. In Innovation in Technology Management-The Key to Global Leadership. PICMET'97: Portland International Conference on Management and Technology. (pp. 206-208). Portland : IEEE.
  42. N. Shehabuddeen, D. Probert & R. Phaal. (2006). From theory to practice: challenges in operationalising a technology selection framework. Technovation, 26(3), 324-335. https://doi.org/10.1016/j.technovation.2004.10.017
  43. G. Montibeller & A. Franco. (2010). Handbook of multicriteria analysis. Berlin : Springer.
  44. N. R. Khalili & S. Duecker. (2013). Application of multi-criteria decision analysis in design of sustainable environmental management system framework. Journal of Cleaner Production, 47, 188-198.
  45. G. A. Kiker, T. S. Bridges, A. Varghese, T. P. Seager & I. Linkov. (2005). Application of multicriteria decision analysis in environmental decision making. Integrated environmental assessment and management, 1(2), 95-108. https://doi.org/10.1897/IEAM_2004a-015.1
  46. J. D. Maloney. (1982). How companies assess technology. Technological Forecasting and Social Change, 22(3), 321-329. https://doi.org/10.1016/0040-1625(82)90070-1
  47. Y. G. Hsu, G. H. Tzeng & J. Z. Shyu. (2003), Fuzzy multiple criteria selection of government sponsored frontier technology R&D projects. R&D Management, 33(5), 539-551.
  48. D. Kutlaca. (1997). Multicriteria-based procedure as decision support in the selection of government financed R&D project. Yugoslav journal of operations research, 7(1), 133-148.
  49. A. L. Medaglia, D. Hueth, J. C. Mendieta & J. A. Sefair. (2008). A multiobjective model for the selection and timing of public enterprise projects. Socio-Economic Planning Sciences, 42(1), 31-45. https://doi.org/10.1016/j.seps.2006.06.009
  50. C. H. Wei & M. C. Chung. (2003). Grey Statistics Method of Technology Selection for Advanced Public Transportation Systems: The Experience of Taiwan. IATSS Research, 27(2), 66-72.
  51. L. S. Kim. (2015). Convergence of Information Technology and Corporate Strategy. Journal of the Korea Convergence Society, 6(6), 17-26. https://doi.org/10.15207/JKCS.2015.6.6.017
  52. T. W. Roh. (2018). GHG Reduction Effect through Smart Tolling: Lotte Data Communivation Company. Journal of Digital Convergence, 16(4), 87-94. https://doi.org/10.14400/JDC.2018.16.4.087
  53. J. C. Shin & K. I. Kim. (2016). A study on the success factors in the Enterprise Information Systems introduced. Journal of Convergence for Information Technology, 6(4), 1-8.
  54. H. G. Hong. (2017). Business Process Support Based on IoT Technology. Journal of Convergence for Information Technology, 7(1), 75-79. https://doi.org/10.22156/CS4SMB.2017.7.1.075
  55. S. Kim, S. Hong, K. Ahn & S. Gong. (2015). Priority survey between indicators and analytic hierarchy process analysis for green chemistry technology assessment. Environmental health and toxicology, 30.
  56. J. Ren & M. Lutzen. (2015). Fuzzy multi-criteria decision-making method for technology selection for emissions reduction from shipping under uncertainties. Transportation Research Part D: Transport and Environment, 40, 43-60.
  57. D. O. Choi, H. K. Lee, J. K. Lim & H. G. Lee. (2009). An Evaluation Model Development of Technology Green Index (TGI) and It's Application to Defense R&D Projects. Journal of the Korea Institute of Military Science and Technology, 12(3), 299-308.
  58. P. Yu & J. H. Lee. (2012). Integrated AHP and DEA Method for Technology Evaluation and Selection: Application to Clean Technology. Knowledge Management Research, 13(3), 55-77.
  59. M. G. Kharat, R. D. Raut, S. S. Kamble & S. J. (2016). The application of Delphi and AHP method in environmentally conscious solid waste treatment and disposal technology selection. Management of Environmental Quality: An International Journal, 27(4), 427-440. https://doi.org/10.1108/MEQ-09-2014-0133
  60. Y. Tang, H. Sun, Q. Yao & Y. Wang. (2014). The selection of key technologies by the silicon photovoltaic industry based on the Delphi method and AHP (analytic hierarchy process): Case study of China. Energy, 75, 474-482.
  61. C. H. Chen, M. C. Chung & C. H. Wei. (2006). Government policy of technology selection for advanced traveler information system. R & D Management, 36(4), 439-450.
  62. M. J. Gregory. (1995). Technology management: a process approach. Proceedings of the Institution of Mechanical Engineers. Part B: Journal of Engineering Manufacture, 209(5), 347-356.
  63. United Nations Development Programme. (2016. April). Developing country support needs for the implementation of nationally determined contributions (NDCs), 1-8.
  64. J. B. Park, B. M. Kim, JIAN. SHEN & D. S. Rho. (2011). Development of Remote Monitoring and Control Device of 50KW Photovoltaic System. Journal of the Korea Convergence Society, 2(3), 7-14.
  65. G. H. Kim, C. S. Yi, G. D. Yeo & M. P. Shim. (2009). Priority decision of small hydropower development using spatial multi-criteria decision making. Journal of Korea Water Resources Association, 42(12), 1029-1038. https://doi.org/10.3741/JKWRA.2009.42.12.1029
  66. J. Mathur, N. K. Bansal & H. J. Wagner. (2003). Investigation of greenhouse gas reduction potential and change in technological selection in Indian power sector. Energy Policy, 31(12), 1235-1244. https://doi.org/10.1016/S0301-4215(02)00184-2
  67. J. C. Kim & S. J. Shim. (2013). A Study on the GHG Reduction Newest Technology and Reduction Effect in Power Generation.Energy Sector. Journal of Climate Change Research 4(4), 12, 349-358.
  68. C. K. Park. (2008). Climate Change; Its Impacts and Our Strategy to Address It. Journal of Korean Society of Environmental Engineers, 30(12), 1179-1182.
  69. J. H. Han & J. H. Kim. (2008). Green Growth as a National Growth Strategy: Concept․Framework․ Issue. Green Growth: Seeking a national growth strategy. Seoul: Korea Development Institute.
  70. S. J. Yoon. (2009). Finding governance to prevent and mitigate social conflicts surrounding climate change response. Journal of governance studies, 4(2), 125-160.
  71. Commission on Green Growth. (2009). Setting the Medium-term(2020) Goal for the National Greenhouse Gas Emission Reduction. Seoul : Commission on Green Growth.
  72. Commission on Green Growth. (2009). Green Growth National Strategy. Seoul : Commission on Green Growth.
  73. UNEP-IETC. (2007). Demonstrating ESTs for Building waste Reduction in Indonesia. The DEBRI Project Technology Identification and Selection. Osaka : UNEP-IETC.
  74. S. A. K. Firouzabadi, B. Henson & C. Barnes. (2008). A multiple stakeholders' approach to strategic selection decisions. Computers & Industrial Engineering, 54(4), 851-865. https://doi.org/10.1016/j.cie.2007.10.015
  75. C. Macharis, A. Verbeke & K. De Brucker. (2004). The strategic evaluation of new technologies through multicriteria analysis: the ADVISORS case. Research in Transportation Economics, 8, 443-462.
  76. S. W. Park. (2016). Post-2020 Climate Regime and Paris Agreement. Environmental Law and Policy, 16, 285-322.
  77. Ministries concerned. (2016). The First Basic Plan for Climate Change Response.
  78. UN Environment, UNEP DTU Partnership, the UNFCCC Secretariat, Unite Nations Development Programme & the World Resources Institute. (2017). Joint NDC Implementation Guidance. http://www.indcsupport.org/ndc-guidance
  79. Wuppertal Institute. (2016. July). The Transition Period Towards implementation of the Paris Agreement, Carbon Mechanisms Review, Issue 2, 1-24.
  80. Ministries concerned. (2016). The 2030 Basic Roadmap for the Reduction of Greenhouse Gas Emissions.
  81. K. Levin et al. (2015). Designing and preparing intended nationally determined contributions (INDCs). Washington, DC: World Resources Institute.