최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기韓國林學會誌 = Journal of Korean Forest Society, v.102 no.4, 2013년, pp.477 - 483
표정기 (국립산림과학원 기후변화연구센터) , 손영모 (국립산림과학원 기후변화연구센터) , 장광민 (한국임업진흥원 산림탄소인증센터) , 이영진 (공주대학교 산림자원학과)
The purpose of this study was to calculate uncertainty of emission factor collected data and to evaluate the applicability of Monte Carlo simulation technique. To estimate the distribution of emission factors (Such as Basic wood density, Biomass expansion factor, and Root-to-shoot ratio), four proba...
핵심어 | 질문 | 논문에서 추출한 답변 |
---|---|---|
불확도란 무엇인가? | 불확도는 알고자하는 미지의 값과 적용되는 값의 차이를 나타내는 수치이고 신뢰성 있는 연구 결과의 제시를 위하여 불확도를 최소화해야 한다(Dieck, 2007; Ramírez et al., 2008). | |
탄소배출계수의 개발 목적은? | , 2010). 산림에 저장된 탄소량을 추정하기 위하여 개발되는 탄소배출계수는 환경과 지형적 차이에 따라 동일 수종에서도 상이하여 탄소저장량의 추정에서 불확도를 높이는 주요한 원인이 된다(Monte et al., 1996; Kangas and Kangas, 2004). | |
IPCC가 제시한 불확도를 계산하는 방법 2가지는 무엇인가? | IPCC(2003; 2006a)는 불확도를 계산하는 방법으로 두 가지를 제시하고 상황에 따라 유연한 계산이 가능하도록 하였다. 접근 1(approach 1)은 오차전달법(error propagation)으로 다양한 부분에서 발생하는 불확도를 합성하여 추정하고, 적용되는 인자의 불확도가 상대적으로 작은 경우와 변수 간의 상관관계가 적은 경우, 사용이 가능하다. 접근 2(approach 2)는 추정된 수치의 불확도가 크거나 변수 간의 상관관계가 존재하는 경우, 몬테 카를로 시뮬레이션(monte carlo simulation)을 이용하도록 권장한다(IPCC, 2006a; Yanai et al., 2010). |
Center for Global Environmental Research. 2011. National greenhouse gas inventory report of JAPAN. Ministry of the Environmental, Japan. pp. 41.
Dagpunar, J.S. 2007. Simulation and monte carlo. John Wiley and Sons. pp. 75.
Dieck, R.H. 2007. Measurement uncertainty methods and application. The instrumentation, systems, and automation society. pp. 39-62.
Fan, X., Felsovalyi, A., Sivo, S.A., and Keenan, S.C. 2001. SAS for monte carlo studies: A guide for quantitative researchers. Cary, North Carolina: SAS Institute Incorporation. pp. 26-41.
Fujiwara. T., Yamashita, K., and Kuroda, K. 2007. Basic densities as a parameter for estimating the amount of carbon removal by forests and their variation. Bulletin of the Forestry and Forest Products Research Institute 6(4): 215-226.
Gertner, G. 1987. Approximating precision in simulation projections: An efficient alternative to monte carlo methods. Forest Science 33(1): 230-239.
Intergovernmental panel on climate change. 2000. Good practice guidance and uncertainty management in national greenhouse gas inventories. IPCC National Greenhouse Gas Inventory Programme. Institute for Global Environmental Strategies. pp. 6.1-6.23.
Intergovernmental panel on climate change. 2003. Good practice guidance for land use, land-use change and forestry. IPCC National Greenhouse Gas Inventory Programme. Institute for Global Environmental Strategies. pp. 5.8-5.9.
Intergovernmental panel on climate change. 2006a. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Volume 1. General Guidance and Reporting. IPCC National Greenhouse Gas Inventory Programme. Institute for Global Environmental Strategies. pp. 3.6-3.78.
Intergovernmental panel on climate change. 2006b. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Volume 4. Agriculture, Forestry and Other Land Use. IPCC National Greenhouse Gas Inventory Programme. Institute for Global Environmental Strategies. pp. 4.73.
Jung, H.J. 2011. Sensitivity and uncertainty analysis of forest carbon stock changes in Korea. M.D. thesis, Graduate School of Public Health, Seoul National University. pp. 59.
Kalos, M.H. and Whitlock, P.A. 2008. Monte carlo methods, second, revised and enlarged edition. WILEY-VCH Verlag GmbH and Corporation, KGaA, Weinheim. pp. 9.
Kangas, A.S. and Kangas, J. 2004. Probability, possibility and evidence : approaches to consider risk and uncertainty in forestry decision analysis. Forest Policy and Economics 6: 169-188.
Korea Forest Research Institute. 2010. The carbon emission factor of major species for national inventory by forestry in Korea. Korea Forest Research Institute. pp. 7.
Levy, P.E., Hale, S.E., and Nicoll, B.C. 2004. Biomass expansion factors and root:shoot ratios for coniferous tree species in Great Britain. Forestry 77(5): 421-430.
Lehtonen, A., R. Makipaa, J. Heikkinen, R. Sievanen, and J. Liski. 2004. Biomass Expansion factor (BEFs) for Scotes pine, Norway spruce and birch according to stand age for boreal forests. Forest Ecology and Management 188: 211-224.
Monte, L., Lars, H., Ulla, B., John, B., and Rudie, H. 1996. Uncertainty analysis and validation of environmental models : The empirically based uncertainty analysis. Ecological Modelling 91: 139-152.
Monni, S., Peltoniemi, M., Palosuo, T., Lehtonen, A., Makipaa, R., and Savolainen, I. 2007. Uncertainty of forest carbon stock changes-implications to the total uncertainty of GHG inventory of Finland. Climate Change 81: 391-413.
Monni, S., Syri, S., and Savolainen, I. 2004. Uncertainties in the Finnish greenhouse gas emission inventory. Environmental Science and Policy 7: 87-98.
Ramirez, A., Keizer, C., Sluijs, J.P., and Olivier, J. 2008. Monte carlo analysis of uncertainties in the Netherlands greenhouse gas emission inventory for 1990-2004. Atmospheric Environment 42: 8263-8272.
Refsgaard, J.C., Jeroen, P.V.D.S., Anker, L.H., and Peter, A.V. 2007. Uncertainty in the environmental modelling process-A framework and guidance. Environmental Modelling and Software 22: 1543-1556.
SAS Institute, Incorporation. 2012. SAS/STAT 9.3 User's Guide. SAS Institute, Incorporation. Cary. North Carolina. pp. 439.
Winiwarter, W. and Rypdal, K. 2001. Assessing the uncertainty associated with national greenhouse gas emission inventories: a case study for Austria. Atmospheric Environment 35: 5425-5440.
Yanai, R.D., John, J.B., Andrew, D.R., Corrie, A.B., Dustin, M.W., and Edward, B.R. 2010. Estimating uncertainty in ecosystem stock calculations. Ecosystem 13: 239-248.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
Free Access. 출판사/학술단체 등이 허락한 무료 공개 사이트를 통해 자유로운 이용이 가능한 논문
※ AI-Helper는 부적절한 답변을 할 수 있습니다.