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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 馬鴻文(Hwong-Wen Ma) | |
dc.contributor.author | Jun-Pin Su | en |
dc.contributor.author | 蘇俊賓 | zh_TW |
dc.date.accessioned | 2021-06-15T00:15:30Z | - |
dc.date.available | 2009-06-23 | |
dc.date.copyright | 2009-06-23 | |
dc.date.issued | 2009 | |
dc.date.submitted | 2009-06-16 | |
dc.identifier.citation | 參考文獻
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/41302 | - |
dc.description.abstract | 近年來,隨著環境變遷,固體廢棄物產出的質與量不斷改變,其所衍生的相關問題也日益多元,雖有許多廢棄物管理技術、工具已陸續發展,相關工具是否能妥適解決日益複雜的問題,已日益受到關注。如何提供妥適的固體廢棄物管理政策,邁向循環型社會,為本研究所關注的核心問題。
本研究考量政策之環境面、社會面、技術面以及兼顧衝突特性等因素進而提出固體廢棄物管理系統整合模式之修正。此模式較其他固體廢棄物管理模式不同之修正特色有三,第一為本模式以模糊多準則評估模式為架構,建立政策衝擊潛勢分析方法,輔助既有之社會面評估方法。第二為本模式為了有利區域之間同類型固體廢棄物政策之經驗移轉,提出以區域資料庫為基礎,結合類神經網路與複回歸推估技術,作為評估之依據。第三為本模式增加中長程整合性推估與決策功能,修正目前之廢棄物管理模式較欠缺整合性之問題。 本研究整合上述三項修正,在決策規則輔助下建立具有中長程整合預測功能、區域經驗能有效移轉以及以政策衝擊潛勢方法輔助社會面評估之固體廢棄物政策支援系統。並以桃園縣垃圾費隨袋徵收、台北市垃圾焚化飛灰再利用、以及台北縣廢棄物中長程規劃等三個案例進行模式之案例研究,驗證本方法之實用性。 案例研究結果顯示,政策衝擊潛勢分析適度輔助決策支援系統,可供決策者較多元之參考依據,而在區域資料庫較完備的情形下,類神經網路結合複回歸預測,的確有助於不同區域之間廢棄物政策之經驗移轉,而面對廢棄物政策之整合性需求,本研究所建立之中長程廢棄物評估方法為可考量之決策評估方法。本研究以系統軟體Stella為工具,建立永續廢棄物政策決策支援系統。 | zh_TW |
dc.description.abstract | Both the quantity and quality of municipal solid waste (MSW) have changed with time. The problems of municipal solid waste management (MSWM) become multiple. While many technologies and models on waste management have been developed, more and more attentions have been paid to whether these tools are appropriate to solve increasingly complex problems. The core concern of this research is to deal with the issues of how to plan a sustainable waste management policy for Taiwan for the purpose of approaching.
An improved integrated model for solid waste management has been provided in this study. This model has considered and integrated the environmental, social, and technical aspects of waste policies as well as the characteristic of potential conflicts. Compared to other models, this model has three characteristics as follows. First, fuzzy MCDM has been used in this model as a basic framework to build the policy impact potential analysis (PIPA) method to assist in the assessment of influences from social conflict toward each case. Second, the SOM neural network is combined with multiple regression for estimating MSW based on a regional database to assist in the transfer of regional experience. Third, in order to enhance integration, the mid- and long-term planning is also incorporated in the model. This study tried to set up a policy decision-making support system. The system can provide a long-term forecast function, an model on transfer of regional experiences, and an analysis of potential policy impacts. This study examined the practicability of the model by testing case studies, including “fly ash regeneration in Taipei city”, “regional PAYT policy assessment in Taoyuan county”, and “mid-term and long-term regional waste treatment policies in Taipei county”. The results of the case studies showed that policy impact potential analysis (PIPA) can be valuable to the decision-making support system. It can provide decision-makers with guidelines of communication when other information was not available. The SOM neural network and the multiple regression for estimating MSW was useful for the transfer of regional experience if the regional database was sufficient. The integrated long-term model has also been demonstrated to be one of the models to idenfify the key points of long-term waste management. This study used Stella as tool for building the improved Decision Support System of Sustainable Waste Management. | en |
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dc.description.tableofcontents | 目 錄
口試委員會審定書 i 誌謝 ii 中文摘要 iii 英文摘要 iv 第一章 緒論 1 1.1 研究背景 1 1.2 研究目的 2 1.3 研究流程 2 第二章 環境政策之檢討分析 4 2.1 區域環境政策之類比分析 5 2.1.1 區域廢棄物政策效益分析 5 2.1.2 區域社會經濟資料庫與環境資料庫 7 2.2 環境政策之社會衝擊 9 2.2.1 台灣環境政策之統計分析 9 2.2.2 媒體衝擊分析 11 2.3 環境政策之中長程規劃 12 2.3.1 台灣廢棄物產生量之推估檢討分析 12 2.3.2 台灣廢棄物產生量之推估校正需求 14 2.4 本章小結 14 第三章 文獻回顧 17 3.1 廢棄物管理之演進 17 3.1.1 廢棄物管理概念演變 17 3.1.2 廢棄物管理決策技術之演進 19 3.1.3 廢棄物管理系統之比較 21 3.2 類神經網路 24 3.2.1自組織特徵映射網路之理論 24 3.2.2自組織特徵映射網路之應用 24 3.3社會衝擊評估方法 25 3.4 廢棄物產量推估 29 3.4.1 複回歸 30 3.4.2 其他推估 30 3.4.3 廢棄物產量推估應用於物質流 31 3.4.4 廢棄物產量推估工具之發展需求 32 3.5 多準則決策分析 32 3.5.1 多準則決策之分類 33 3.5.2 分析層級程序法 33 3.6決策支援系統 36 3.6.1 決策支援系統之類別與特性 36 3.6.2 決策規則應用於廢棄物管理 40 第四章 研究方法 41 4.1 研究方法概述 41 4.2 類神經網路結合區域環境政策資料庫推估方法之建立 42 4.2.1 SOM類神經網路 42 4.2.2 TOPSIS 44 4.3 政策衝擊潛勢分析方法之建立 47 4.3.1 政策衝擊準則 47 4.3.2 政策衝擊評估潛勢之量化 48 4.3.3 多準則決策分析與政策衝擊潛勢分析之整合 50 4.4 物質流推估方法用於中長程多準則決策分析方法 51 4.5 模糊AHP分析 54 4.6 以決策規則進行研究流程之校正 58 第五章 類神經網路結合區域環境資料庫推估方法之建立 -桃園縣之垃圾費隨袋徵收政策評估 59 5.1 案例說明 59 5.2 PAYT於各地區之應用 60 5.3 類神經網路用於區域廢棄物產量預測 62 5.3.1 SOM分群結果 62 5.3.2 複回歸方式預測結果 65 5.3.3 推估結果 68 5.4 結合多準則決策分析與類神經網路之決策支援系統 69 5.4.1 建立環境面之生命週期評估模式 70 5.4.2 TOPSIS分析 78 5.5 本章小結 81 第六章 政策衝擊潛勢分析案例研究 -台北市飛灰再利用政策 83 6.1 案例說明 83 6.2 政策衝擊潛勢分析於台北市飛灰再利用管理之應用 84 6.2.1 優先方案之選擇 84 6.2.2 政策衝擊潛勢分析(policy impact potential analysis) 87 6.2.3 資訊分析與決策 88 6.3 本章小結 90 第七章 以MCDM 物質流進行中長程廢棄物政策規劃 -以桃園縣為例 91 7.1 案例說明 91 7.2 物質流推估用於桃園縣中長程廢棄物政策之應用 92 7.3 結合多準則決策分析與物質流推估之決策支援系統 92 7.3.1中長程方案之物質流推估 92 7.3.2 MCDM評估 96 7.3.3 評估結果 97 7.4 本章小結 101 第八章 永續廢棄物管理決策支援系統之建立 103 第九章 結論與建議 110 9.1 結論 110 9.2 建議 111 參考文獻 附錄一 台北市焚化飛灰再利用案例準則重要性問卷 附錄二 台北市焚化飛灰再利用案例績效值界定問卷 附錄三 台北市焚化飛灰再利用政策衝擊潛勢準則評比問卷 附錄四 桃園縣PAYT案例準則重要性問卷 附錄五 桃園縣PAYT案例績效值界定問卷 圖 目 錄 內容 頁次 圖1.3-1 研究流程 3 圖2.1-1 台灣主要縣市垃圾清運量減量率比較 6 圖2.1-2 垃圾費隨袋徵收政策對於垃圾產生量之影響比較 7 圖2.2-1 廢棄物處理設施興建與否與新聞報導之關係 12 圖2.3-1 台灣地區近20年垃圾推估量與實際產量之比較 13 圖2.3-2 過去二十年台灣之垃圾清運量 14 圖3.1-1 階層式廢棄物管理 18 圖3.1-2 整合式廢棄物管理策略 18 圖3.1-3 廢棄物管理決策技術之演進 20 圖3.6-1 觀念上決策支援系統架構 37 圖4.1-1 研究方法 41 圖4.2-1 區域經驗移轉研究架構研究架構 44 圖4.3-1 結合模糊AHP法與PIPA之演算步驟 51 圖4.4-1 中長程政策評估研究架構 53 圖4.5-1 語意變數之模糊數定義 57 圖4.6-1決策規則流程示意圖 58 圖5.3-1 桃園縣各鄉鎮市、石岡鄉與台北市各行政區SOM分群結果 64 圖5.3-2 桃園市、中壢市、平鎮市、八德市及台北市各行政區SOM分群結果 65 圖5.3-3 桃園市經垃圾隨袋徵收後垃圾產量變化趨勢圖 67 圖5.3-4 以複回歸模式推估桃園縣垃圾清運量變化趨勢 68 圖5.4-1 桃園縣隨袋徵收可行性評估架構 70 圖5.4-2 台灣廢棄物管理系統邊界 72 圖5.4-3 人體相關環境衝擊評估結果 77 圖5.4-4 生態相關環境衝擊評估結果 77 圖5.4-5 全球性環境衝擊評估結果 78 圖6.2-1 飛灰管理問題之準則層級分析圖 84 圖6.2-2 飛灰管理再利用方案政策衝擊潛勢評估結果 88 圖7.1-1 桃園縣歷年垃圾處理方式比例變化 91 圖7.2-1 2006年桃園縣一般廢棄物物質流佈 92 圖7.3-1 中長程維持現狀方案物質流佈 94 圖7.3-2 中長程著重源頭減量方案物質流佈 95 圖7.3-3 中長程著重資源回收方案物質流佈 95 圖7.3-4 中長程著重中間處理改善方案物質流佈 96 圖7.3-5 中長程廢棄物政策之評估架構 97 圖7.3-6 中長程策略規劃方案各環境衝擊類別評估結果 100 圖7.3-7 中長程策略規劃評估結果雷達圖 101 圖8.1 SWMDSS進入畫面 103 圖8.2 SWMDSS架構 104 圖8.3 問題類型界定輸入頁面 106 圖8.4 廢棄物產量輸入頁面 106 圖8.5 績效值評估系統頁面 107 圖8.6 準則權重評估頁面 108 圖8.7 決策方法選擇頁面 109 圖8.8 評估結果頁面 109 表 目 錄 內容 頁次 表2.1-1 台灣地區一般廢棄物資料庫 8 表2.1-2 台灣地區事業廢棄物資料庫 8 表2.1-3 主要國家城市背景資料庫 9 表2.2-1 廢棄物處理設施興建媒體報導分析結果 10 表2.4-1 台灣地區各縣市推行之廢棄物政策 15 表3.1-1 廢棄物管理系統分析方法文獻 22 表3.3-1 LCA-IWM團隊所研擬社會指標 27 表3.3-2 台灣廢棄物政策考量社會影響因子 28 表3.4-1 廢棄物產量推估方法之整理 29 表3.4-2 產量推估因子 32 表3.5-1 AHP法之評估與執行步驟 34 表3.5-2 AHP法之優缺點 35 表3.6-1 決策支援系統的特性 39 表4.3-1 政策衝擊潛勢評估準則說明 47 表5.2-1 世界各國垃圾清理費收費方式一覽表 61 表5.3-1 SOM分群各行政區參數輸入資料 63 表5.3-2 桃園縣鄉鎮市與台北市各區以及石岡鄉之相似分群結果 65 表5.3-3 台北市中山區複回歸模式參數 66 表5.3-4 以台北市中山區複迴歸模式預測桃園市隨袋徵收後垃圾趨勢變化 67 表5.3-5 不同隨袋徵收方案垃圾產生情形推估結果 68 表5.4-1 桃園縣隨袋徵收各準則權重 70 表5.4-2 TWMLCA衝擊類別與特徵化模式 73 表5.4-3 隨袋徵收可行性方案環境衝擊評估結果 76 表5.4-4 隨袋徵收方案反應成本比例計算結果 79 表5.4-5 隨袋徵收方案經濟面量化準則評估結果 79 表5.4-6 隨袋徵收方案質化準則評估結果 79 表5.4-7 隨袋徵收方案可行性評估量化與質化準則結果 80 表5.4-8 隨袋徵收方案可行性評估結果(TOPSIS) 81 表5.4-9 隨袋徵收方案可行性評估結果(平均權重) 81 表6.2-1 飛灰管理再利用方案各項準則之模糊合併權重 85 表6.2-2 飛灰管理再利用方案統計結果 86 表6.2-3 飛灰管理再利用方案敏感度分析結果 87 表6.2-4 飛灰管理再利用方案的模糊績效值 89 表7.3-1 中長程策略規劃方案說明 94 表7.3-2 中長程策略規劃方案環境面評估結果 98 表7.3-3 中長程策略規劃評估結果 101 | |
dc.language.iso | zh-TW | |
dc.title | 固體廢棄物管理系統整合模式之修正研究 | zh_TW |
dc.title | An Improved Integrated Model for Solid Waste Management | en |
dc.type | Thesis | |
dc.date.schoolyear | 97-2 | |
dc.description.degree | 博士 | |
dc.contributor.coadvisor | 於幼華(Yue-Hwa Yu) | |
dc.contributor.oralexamcommittee | 李公哲(Kung-Cheh Li),張祖恩(Juu-En Chang),林素貞(Sue-Jane Lin) | |
dc.subject.keyword | 多準則決策,模糊層級分析法,垃圾費隨袋徵收,類神經網路,廢棄物管理,決策支援系統, | zh_TW |
dc.subject.keyword | Multiple Criteria Decision Making,Fuzzy Analytic Hierarchy Process (Fuzzy AHP),PAYT,SOM neural network,waste management,decision support system, | en |
dc.relation.page | 111 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2009-06-16 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 環境工程學研究所 | zh_TW |
顯示於系所單位: | 環境工程學研究所 |
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