Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
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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
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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.

국가 온실가스 감축수단의 확보를 위해 전략적으로 감축기술을 선택하는 것은 매우 중요한 문제이다. 특히, 공공의 기술선택은 다양한 요인들을 고려하는 포괄적인 접근의 필요성이 강조된다. 그러나 지금까지의 논의들은 기술적, 경제적 요인에 중심을 두고 기술선택을 다루고 있으며, 일관된 선택 기준이 적용되지 않고 있다. 본 연구는 국가 온실가스 감축기술을 선택하기 위한 지표를 개발하고자 전문가 22인을 대상으로 델파이 기법을 사용하였다. 이를 통해 타당성이 확보된 국가 온실가스 감축기술 선택 지표를 개발하였으며, 기술, 경제, 환경, 정책, 사회의 관점을 종합적으로 반영하는 것에 대한 적절성을 확인하였다. 상용화기술은 5개 항목의 16개 지표, 신기술은 5개 항목의 18개 지표로 구성되었다. 본 연구를 통해 제시된 기술선택 지표는 국가 온실가스 감축기술 선택을 위한 의사결정 도구로 활용될 수 있을 것이며, 국가 온실가스 감축기술의 적용과 확보에 관한 판단기준 연구에 토대를 제공할 것으로 기대한다.

Keywords

References

  1. S. W. Park. (2016). Post-2020 Climate Regime and Paris Agreement. Environmental Law and Policy, 16, 285-322. https://doi.org/10.18215/envlp.16..201602.285
  2. Ministries concerned. (2016). The First Basic Plan for Climate Change Response.
  3. 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
  4. Wuppertal Institute. (2016. July). The Transition Period Towards implementation of the Paris Agreement, Carbon Mechanisms Review, Issue 2, 1-24.
  5. Ministries concerned. (2016). The 2030 Basic Roadmap for the Reduction of Greenhouse Gas Emissions.
  6. K. Levin et al. (2015). Designing and preparing intended nationally determined contributions (INDCs). Washington, DC: World Resources Institute.
  7. United Nations Development Programme. (2016. April). Developing country support needs for the implementation of nationally determined contributions (NDCs), 1-8.
  8. 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.
  9. 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
  10. 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
  11. 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.
  12. 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.
  13. 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.
  14. S. J. Yoon. (2009). Finding governance to prevent and mitigate social conflicts surrounding climate change response. Journal of governance studies, 4(2), 125-160.
  15. Commission on Green Growth. (2009). Setting the Medium-term(2020) Goal for the National Greenhouse Gas Emission Reduction. Seoul : Commission on Green Growth.
  16. Commission on Green Growth. (2009). Green Growth National Strategy. Seoul : Commission on Green Growth.
  17. UNEP-IETC. (2007). Demonstrating ESTs for Building waste Reduction in Indonesia. The DEBRI Project Technology Identification and Selection. Osaka : UNEP-IETC.
  18. 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
  19. 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. https://doi.org/10.1016/S0739-8859(04)08019-9
  20. 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
  21. 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
  22. 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.
  23. 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
  24. 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.
  25. 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. https://doi.org/10.1016/j.trd.2015.07.012
  26. 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.
  27. 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.
  28. 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
  29. 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. https://doi.org/10.1016/j.energy.2014.08.003
  30. 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. https://doi.org/10.1111/j.1467-9310.2006.00445.x
  31. 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. https://doi.org/10.1243/PIME_PROC_1995_209_094_02
  32. 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. https://doi.org/10.1016/S0924-0136(00)00679-8
  33. P. Dussauge, S. Hart & B. Ramanantsoa. (1992). Strategic technology management. Wiley.
  34. 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
  35. 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.
  36. 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
  37. G. Montibeller & A. Franco. (2010). Handbook of multicriteria analysis. Berlin : Springer.
  38. 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. https://doi.org/10.1016/j.jclepro.2012.10.044
  39. 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
  40. 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
  41. 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. https://doi.org/10.1111/1467-9310.00315
  42. 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.
  43. 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
  44. 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. https://doi.org/10.1016/S0386-1112(14)60145-X
  45. 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.
  46. 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.
  47. 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.
  48. 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.
  49. 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.
  50. 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. https://doi.org/10.7763/JOCET.2015.V3.193
  51. 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.
  52. 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
  53. J. S. Lee. (2001). The Delphi Method. Seoul : KYOYOOKBOOK.
  54. 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.
  55. S. Y. Roh. (2006). Delphi technique: forecasting based on professional intuition. Planning and Policy, 53-62.
  56. 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. https://doi.org/10.5762/KAIS.2011.12.3.1270
  57. 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. https://doi.org/10.1016/0041-1647(76)90094-0
  58. 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
  59. 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
  60. 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
  61. 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.
  62. 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. https://doi.org/10.7235/hort.2012.12037
  63. 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
  64. D. R. Tojib & L. F. Sugianto. (2006). Content validity of instruments in IS research. Journal of Information Technology Theory and Application, 8(3), 5. https://doi.org/10.1080/15228053.2006.10856086
  65. 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.
  66. 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
  67. 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
  68. 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.
  69. 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
  70. N. Dalkey, B. Brown & Cochran. (1970). Use of Self-ratings to Improve Group Estimates : Experimental Evaluation of Delphi Procedures. Technological Forecasting, 1, 283-291. https://doi.org/10.1016/0099-3964(70)90029-3
  71. J. W. Murry & J. O. Hammons. (1995). Delphi: A versatile methodology for conducting qualitative research. The Review of Higher Education, 18(4), 423-436. https://doi.org/10.1353/rhe.1995.0008
  72. 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. https://doi.org/10.15753/aje.2013.14.4.003
  73. 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.
  74. 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.
  75. 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. https://doi.org/10.1080/10962247.2016.1162226
  76. 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.
  77. 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.
  78. 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.
  79. 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.
  80. 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.
  81. 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