| Summary: | 博士 === 國立臺灣大學 === 環境工程學研究所 === 93 === The traditional life cycle assessment (LCA) does not perform quantitative uncertainty analysis. Without characterizing the associated uncertainty, however, the reliability of assessment results cannot be ascertained. The uncertainty analysis also provides useful information to assess the reliability of LCA-based decisions and to determine the need of adding data collection or research toward reducing uncertainty. This study focuses on developing the systematic approach of probabilistic uncertainty analysis of the LCA. The purpose of the study is threefold: first, to identify types and sources of uncertainty in LCA in order to understand the importance of uncertainty issues; second, to develop probabilistic uncertainty analysis that quantifies uncertainty in impact assessment model of LCA and reduce the uncertainty of LCA results with statistic and sites-specific information; lastly, to perform integrated analysis that quantifies combined results of different uncertainties (due to model and decision choices) and identify the relative importance of comparing parameter uncertainty.
In this study, the main contribution was to establish the probabilistic uncertainty analysis method that was capable of improving the drawback of lack uncertainty information in the traditional LCA. The method was based on the probability analysis to identify, quantify, reduce and integrate the uncertainty in LCA that was in combination with the method of sensitivity analysis, Bayesian inference, and integrated framework, respectively. A case study of applying the method to the comparison of alternative waste treatment options in terms of global warming potential due to greenhouse gas emissions was presented.
In the case study, the classification of uncertainty was qualitatively divided into three types including parameter uncertainty, model uncertainty and scenario uncertainty. First of all, in the quantities analysis, the traditional LCA was converted to probabilistic model by incorporating the probabilistic analysis to quantify uncertainty. The results indicated that the incorporation of quantitative uncertainty analysis into LCA revealed more information, such as mean value, standard deviation, and complete probability distribution than the deterministic LCA method. The resulting decision may thus be different. In addition, the sensitivity analysis in combination with the Monte Carlo simulation, calculations of the rank correlation coefficients facilitated the identification of important parameters that had major influences to LCA results. The results indicate that the overlaps of probability density functions (pdf) were used to judge the influence on LCA results between alternatives.
Second, in respective of uncertainty reduction, the Bayesian method in combination with the Monte Carlo technique was used to quantify and update the uncertainty in LCA results. In the case study, the prior distributions of the parameters used for estimating emission inventory and environmental impact in LCA were based on the expert judgment from the Intergovernmental Panel on Climate Change (IPCC) guideline and then updated with using the likelihood distributions resulting from both national statistic and site-specific data. The posterior uncertainty distribution of the LCA results was generated using Monte Carlo simulations with posterior parameter probability distributions. The results indicated that by using national statistic data and site-specific information to update the prior uncertainty distribution, the resultant uncertainty (Coefficient of variation) associated with the LCA results was significantly reduced.
Third, in respective of integration of uncertainties, the integrated framework in combination with the Monte Carlo technique was used to identify the importance of structural uncertainties due to model and decision choices, and then to evaluate the combined effect and relative importance of different types of uncertainty. The results indicated that the resultant uncertainty associated with structural uncertainties of the LCA results might be different or reversed. Therefore, the integrated analysis could understand the importance of structural uncertainties to avoid incorrect decision-making with incomplete uncertainty information.
Finally, the scenario analysis of alternative waste management decision was performed under uncertainty. The results indicated that the integrated analysis revealed complete uncertainty information to enhance the application of LCA-based decision. In addition, the guideline of this method could be used to determine the timing, types and method to use the uncertainty information.
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