產品碳足跡計算不確定性分析之研究

魏佩如

doi:10.6841/NTUT.2010.00256

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產品碳足跡計算不確定性分析之研究

A Study on the Uncertainty of Product Carbon Footprinting

魏佩如 , Masters　　Advisor：胡憲倫　　

繁體中文 DOI： 10.6841/NTUT.2010.00256

溫室氣體；碳足跡；不確定性分析；生命週期評估；蒙地卡羅模擬； Green House Gases (GHG) ； Carbon footprint ； Uncertainty Anslysis ； Life Cycle Assessment ； Monte Carlo Simulation

Abstract │ Reference (91) │ Times Cited (12) │ Altmetrics

Abstract 〈TOP〉

Parallel Abstract 〈TOP〉

In the past few years, political actions and media reporting gain public attention to climate change issues and bring the carbon footprinting into spotlight. Moreover, product carbon labeling is becoming popular domestically and worldwide as they seek to provide carbon footprints information to assist consumers comparing different products and making environmental-consciously purchasing decision.  However, there are wide discrepancies in the results of carbon footprinting when applying LCA and other methodologies.  It shows that many pathways and sources throughout the calculation process may create uncertainties.
While ISO 14064-1 (2006) and working draft of ISO 14067-1 (2010) require to assess all types of uncertainty, in practice the uncertainties of Life Cycle Assessment and Product Carbon Footprinting are usually disgarded and ignored.  Therefore, it is necessary to establish an uncertainty analysis for obtaining credible and unquestionable carbon footprint calculation results.
There are various uncertain factors in calculating product carbon footprint; and, it is hard to conduct a complete uncertainty analysis and to quantify it. In most cases, the parameters uncertainty is the only part that can be controlled.  Thus, this study tries to quantify the uncertainty of product carbon footprinting by using life cycle assssment tool. With the emphasis on quantifying the parameter uncertainty due to human factor, via questionnaire investigation this study adopts Monte Carlo Simulation technique to analyze and quantify those uncertainties.
Based on the results from the independent Sample T Test and ANOVA, the differences between carbon footprinting results and respondants with different background can be realized. Furthermore, the Multiple-Regression-Analysis was adopted to do the sensitivity analysis and to identify the most sensitive factors. Finally, through the Monte Carlo Simulation the systematic error can be observed and quantified and to be used as a correction for parameter uncertainty in the product carbon footprinting.

Reference ( 91 ) 〈TOP〉

[1] Bevington, P.R. and Robinson. D.K., Data reduction and error analysis for the physical sciences. Second edition,. New York: McGraw-Hill, Inc., 1992.
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[2] BSI, PAS 2050—specification for the assessment of the life cycle greenhouse gas emissions of goods and services. London: British Standards Institute, 2008.
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[4] Funtowicz, S.O. and Ravetz. J.R., Uncertainty and quality in science for policy, Dordrecht: Kluwer Academic Publishers, 1990.
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[6] Hofstetter, P., Perspectives in life cycle impact assessment. A structured approach to combine models of the technosphere, ecosphere and valuesphere, Dordrecht: Kluwer Academic Publishers, 1998.
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[8] Morgan, M.G. and Henrion M., Uncertainty. A guide to dealing with uncertainty in quantitative risk and policy analysis, Cambridge: Cambridge University Press, 1990.
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Times Cited (12) 〈TOP〉

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