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系統識別號 U0026-2807201410312100
論文名稱(中文) 運用生命週期評估手法支援創新技術發展——以白蝦生態養殖個案為例
論文名稱(英文) Supporting prospective life cycle assessment of innovations- A case on ecological method in shrimp farming
校院名稱 成功大學
系所名稱(中) 環境工程學系
系所名稱(英) Department of Environmental Engineering
學年度 102
學期 2
出版年 103
研究生(中文) 張恒毅
研究生(英文) Heng Yi Teah
電子信箱 prometheus0304@hotmail.com
學號 P56017058
學位類別 碩士
語文別 英文
論文頁數 92頁
口試委員 指導教授-福島康裕
口試委員-平尾雅彥
口試委員-辛致煒
口試委員-張行道
中文關鍵字 生命週期評估  不確定性分析  決策分析  白蝦養殖 
英文關鍵字 life cycle assessment  uncertainty evaluation  decision support  shrimp aquaculture 
學科別分類
中文摘要 近年來,業界創新技術的發展日新月異。新技術旨在於精進、改良、替代舊技術,但專注於單一項目的創新往往忽略了其他面向的影響,而導致顧此失彼、矯枉過正。
運用生命週期評估手法可有系統地探討某技術發展對產品或服務不同生命階段的環境與經濟影響,進而評估該技術引進所帶來的整體效益。然而,對於支援未發生或模擬性質的評估,其適用性將受限於不確定因素而難以可靠的支援決策。
本研究旨在開發生命週期評估的使用手法,以支援創新技術的不確定性分析。其目的是為了讓決策者對新技術未來發展有更全面的瞭解,並得以作出平衡環境影響與經濟效應的決定,以實踐永續發展的理念。其活動包括:建立生命週期評估模型、分析模型的不確定性、提出生命週期評估另類的支援不確定性分析方法。
本研究的亮點在於所提出的不確定性分析支援手法整合了專家與決策者的意見,使不確定性得以更妥當地評估;傳統分析方法以敏感度、統計學等的分析作探討,缺乏人性化地經驗評估,故對農業、養殖漁業等領域較不實用。
本研究以一台南白蝦養殖戶所新開發的生態養殖工法作為個案探討,以示範說明所提出的不確定性分析支援手法。該個案的目標為支援兩項參數的決定:異營菌投入頻率和飼料投入量,以改善其工法。本研究運用生命週期評估建立環境與經濟成本的等量曲線,使養殖戶僅需判斷單一變數(蝦子產量)就能預估其工法的環境與經濟效益。另外,本研究結合樹狀決策分析與等量曲線建立供養殖戶預估不同的飼料減量方案效益的工具,並同樣讓養殖戶僅需判斷單一變數(各蝦子產量的機率)。此兩項手法皆是質性探討,其結果非量化顯示以供決策者參考。
本研究所提出的研究架構、分析手法將適用於其他創新技術的決策分析,尤其有助於刺激農業、養殖業等相對依賴專家經驗判斷的研發產業;惟使用上需充分瞭解個案中不確定性因素的特質,以及專家或決策者判斷的依據。
英文摘要 Technology innovations are common in nowadays. They are intended to improve the old technology. However, innovations that targeting on one particular improvement, may not result positively in overall. Therefore, introduction of prospective life cycle assessments to systematically examine the economic and environmental impact of products/services life cycle can support the decision-making. The challenge for predicting future change is the uncertainty evaluation. This study proposes two alternative LCA applications, which are the development of indifferences curve and decision-tree analysis, to treat the uncertainty. The methods highlight the incorporation of expert judgment and stakeholder involvement in the uncertainty evaluation processes. A case study of ecological method in shrimp farming is chosen to demonstrate the proposed LCA approaches.
In the innovation of case study, input of heterotrophic bacteria with revised feeding strategy improves aqua-cultural pond ecology; thereby expected to reduce virus outbreaks, main cause of unstable shrimp production. Currently, no standard application guideline for this practice exists. Thus, uncertainties in its economic and environmental effectiveness hold back implementation of this innovative farming practice. This study present decision tools developed to support experts (i.e., farmers) in tailoring the application strategy to respective ponds. Based on expert judgments on expected effectiveness, our figures clarify decision criteria in cost and life cycle environmental impact (GHG emission in this study) in an integrated manner.
First, a cradle-to-gate, stand-alone LCA is performed to benchmark the contribution of the new ecological practice in terms of cost and GHG emission for shrimp production, based on inventory data of farm-scale experiment and literatures. To allow farmers to explore alternatives in bacteria input and feeding strategies, 1) a indifferences curve, or tipping-point-curve, which shows the required productivity enhancement to outweigh additional economic and environmental cost on the basis of benchmark practice and 2) a decision-tree analysis, or figure for two-way sensitivity with three potential outcomes are developed.
The assessment result shows that fresh shrimp at farm gate cost 161 TWD kg-1 and is associated with 3.977 kg-CO2eq. kg-1 in benchmark application. The indifferences curve (see Figure 5-7) shows for example, increase in bacteria frequency to a 20 day-interval expecting a 1% increase in production is worthy in terms of GHG emission reduction but does not pay off in terms of cost. The decision tree analysis (see Figure 5-12) for feed altering strategies presents the cost effective domain and low GHG domain in overlapped layers. It shows either the interest of cost and GHG benefits is consistent or trade-off is required for each intended strategy.
Any decision must be made under some degree of uncertainty. These approaches delegate the role of uncertainty judgment to the experts, while taking responsibilities on removing the complexity due to expansion of scope across the product lifecycles. They are particularly useful in agri-/aqua- cultural studies, because acquiring sufficient volume of data for highly variable environments consumes years of time. Farmers’ experiences and implicit knowledge are assumed to be the best available alternative to scientifically valid data in most of the cases.
論文目次 摘要 II
Abstract IV
Acknowledgement VI
Table of Contents VIII
Index of Tables X
Index of Figures XI
List of Abbreviation XIV
Chapter 1 Introduction 1
1.1 Preface 1
1.2 Objective 6
Chapter 2 Literature Review 9
2.1 Uncertainty evaluation in LCA 9
2.1.1 Characteristic 10
2.1.2 Treatments 14
2.2 LCA thematic study on aquaculture 19
2.2.1 Overview 19
2.2.2 Cases on shrimp farming 20
Chapter 3 Methodology 27
3.1 LCA modeling for benchmarking innovation 27
3.2 Sensitivity analysis for innovation 35
3.3 Proposed uncertainties evaluation in innovation 36
3.3.1 Indifference curve 36
3.3.2 Decision tree analysis 39
Chapter 4 Case study: Ecological shrimp farming 41
4.1 Background 41
4.2 Farming methods 42
4.2.1 Conventional methods 42
4.2.2 Ecological methods 43
4.3 Uncertainty in innovation 48
4.4 LCA modeling for benchmarking ecological method 51
4.5 Sensitivity analysis for ecological method 58
4.6 Proposed uncertainties evaluation in ecological method 61
4.6.1 Indifferences curve – support bacteria addition frequency 61
4.6.2 Decision tree analysis – support feed input amount 63
Chapter 5 Result and discussion 70
5.1 The implementation of innovations 70
5.2 The effect of paradigm shift in uncertainty evaluation 74
5.3 The applicability and limitation of developed tool 79
Chapter 6 Conclusion 80
References 81
Appendix I 86
Appendix II 87
Appendix III 88
Appendix IV 89
Appendix V 90
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