臺灣博碩士論文加值系統

公共污水處理廠作為污水下水道系統最後處理設施，透過去除水中污染物來幫助保護周圍的水環境，近年來亦有許多國家為加強污水處理做出巨大努力。我國自1992年起推動污水下水道系統建設計畫，至今已邁入第六期計畫。然而，建設至今污水下水道系統及公共污水處理廠數量亦日益增加，考量維持系統正常運作已需投入大量人力及經費，近年政府更是朝向循環永續、多元發展推動之政策，未來如何使污水處理廠有效營運，將是系統重要關鍵。
鑑於我國每年度投入百億預算以推動污水下水道建設計畫，後續為配合各項政策及發展目標，應瞭解目前公共污水處理廠營運績效，以更有效分配整體政府的投入資源。為此研究中運用資料包絡分析法(Data Envelopment Analysis, DEA)、麥氏生產力指數(Malmquist productivity index, MPI)，分別評估公共污水處理廠各年度整體效率(Overall Efficiency, OE)、差額變數分析(Slacks-Based Measure)及跨期生產力分析，並透過影響因子及敏感度分析評估影響效率之關鍵因素。
本研究以我國目前營運中15座中型公共污水處理廠(設計處理污水量介於5,000～30,000 CMD)作為評析對象，並選定營運費用、負荷比、用電量等3類作為投入項、COD削減量及SS削減量等2類作為產出項，運用DEA分別計算2015-2019年各年度各廠整體效率(OE)、技術效率(Technical Efficiency, TE)及規模效率(Scale Efficiency, SE)，並依據其參考次數分類。結果顯示林口、石門、石岡壩、六堵等4座廠5個年度均呈現相對有效，且林口及六堵等2座廠為強勢效率單位，亦即4廠在操作及維護上都有一定管理制度，讓污水廠投入經費、人力有效利用，並達成放流處理標準，其中林口及六堵更可以做為典範。另差額變數分析結果顯示，自2018年起為達有效值需調整之廠各數及幅度均提高，代表各廠相對效率差異性越來越大。
因應DEA無法了解跨時間尺度之效率變動情況，本研究利用麥氏生產力指數分析評估2015-2019年跨期各廠效率與生產力變動(亦即效率跨年度變動情況)，結果顯示整體平均MPI值低於1，代表整體生產力(營運效率成長趨勢)呈現下降狀態，其中由各期整體MPI值發現，有統計上顯著下降趨勢，顯示相關操作及維護管理制度作為對效率影響正在削弱，未因應時間變動進行調整。近一步評析各廠平均變動情況，近6成生產力呈現提升，代表大多數廠營運效率是正向發展，餘竹東、斗六、六堵等3廠呈現大幅衰退情況，嚴重影響整體平均MPI值；竹東、斗六廠是在操作面上效率有大幅度衰退，建議瞭解進流水質變動或適時調整操作參數以提昇效率，六堵廠則是在維護面效率大幅降低，有可能是設備老舊未即時更新影響。
本研究另以2019年各廠整體效率及總要素生產力作為分組標的，將15座廠分四組說明，其中林口、石門、石岡壩、虎尾寮及二林等5座廠營運效率(操作及維護情況)良好，且近5年生產力(營運效率成長趨勢)相對為進步，具操作面及維護面管理制度上之優勢，可作為其他廠參考之典範；大樹、斗六及擴大縣治等3座廠營運效率相對排名較後，且近5年生產力(營運效率成長趨勢)有進步緩慢或退步之情況，初步評估三廠因都有進流水量偏低之情況，造成在操作面上的效率無法提升，建議後續應加速進流水量之提升。
在影響因子部分，以設計處理污水量、污水處理廠運轉年齡、處理程序是否具備除氮功能、區位特性及COD的濃度進行K-W檢定( Kruskal-Wallis Test)探討，結果顯示處理程序是否具備除氮功能及COD濃度有統計上差異，其餘3項則無統計上差異，代表公共污水處理廠處理程序是否具備除氮功能及初始進流濃度是影響整體效率之關鍵外部因子。本研究另為了解投入項敏感度，以投入值增減10%、其餘值不動情況下計算整體效率變動情形，結果顯示營運費增加將使整體效率降低，營運費減少會影響效率，但趨勢則不一定，而負荷比及用電量則不影響整體效率。

Domestic sewage treatment plants serve as the final treatment facilities for urban sewage systems, helping to keep safe the nearby water environment by taking away pollutants from the water. In recent years, many countries have also made significant efforts to strengthen sewage treatment immediate development. Since 1992, our country has been promoting the construction of urban sewage systems, and we are currently in the sixth phase of the plan. However, the number of systems and treatment plants has been increasing, and it requires a large amount of manpower and cost to maintain the operation. In the near future, the government has also been promoting policies for sustainable and diversified development. Ensuring the effective operation of sewage treatment plants will be a crucial issue for the system.
In view of the fact that our country commits tens of billions budgeted per year for urban sewage systems construction. In order to comply with various policies and development goals, it is necessary to understand the current operational performance of domestic sewage treatment plants to allocate overall government resources more effectively. Therefore, this study employs Data Envelopment Analysis (DEA) and the Malmquist productivity index (MPI) to evaluate the overall efficiency, difference variables, and intertemporal productivity analysis of public sewage treatment plants each year, and assess the key factors affecting efficiency through factor and sensitivity analysis.
This study focuses on 15 medium-sized domestic sewage treatment plants in our country (with a designed treatment capacity ranging from 5,000 to 30,000 CMD) as the evaluation objects. Operating costs, load ratio, and electricity consumption are selected as input, while COD reduction and SS reduction are selected as output . DEA model is given to calculate the overall efficiency, technical efficiency, and scale efficiency of each plant for years 2015-2019, and classify them according to their reference times. The results show that the four plants, Linkou, Shimen, Shigang Dam, and Liudu, have all shown relative effectiveness in five consecutive years. Linkou and Liudu were identified as strong efficiency units, indicating that these four plants have certain management systems in place for operation and maintenance, allowing for effective utilization of funds and manpower, and achieving the discharge treatment standards. Linkou and Liudu can be considered as exemplary cases. Furthermore, the analysis of the difference variables showed that the number and magnitude of adjustments required to achieve the effective value have increased since 2018, indicating that the relative efficiency differences among the plants are becoming larger.
In response to the DEA's inability to understand the efficiency changes across different time periods, this study uses the Malmquist Productivity Index to analyze and evaluate the efficiency and productivity changes across different years from 2015 to 2019 (i.e., the efficiency changes across years). The results show that the overall average MPI value is below 1, indicating a decline in overall productivity (operational efficiency growth trend). Among the overall MPI values for each period, there is a statistically significant downward trend, indicating that the impact of relevant operations and maintenance management systems on efficiency is weakening and not adjusted for time changes. Further analysis of the average changes in each factory shows that nearly 60% of the productivity has improved, indicating that the operational efficiency of the majority of factories is developing positively. However, Yuzhu East, Douliu, and Liudu factories show a significant decline, severely affecting the overall average MPI value. Yuzhu East and Douliu factories have experienced a significant decline in efficiency in terms of operations, suggesting the need to understand changes in influent water quality or adjust operational parameters in a timely manner to improve efficiency. Liudu factory, on the other hand, has experienced a significant decrease in efficiency in terms of maintenance, possibly due to outdated equipment that has not been updated in a timely manner.
Because of the DEA model inability to understand the efficiency changes across different time periods, this study uses the Malmquist Productivity Index to evaluate the efficiency and productivity changes of various factories from 2015 to 2019. The results show that the overall average MPI value is below 1, indicating a decline in overall productivity. Among the overall MPI values for each period, there is a statistically significant downward trend, indicating that the impact of relevant systems and regulations on efficiency is weakening, suggesting that the relevant regulations and systems have not been adjusted to accommodate time changes. Analysis of the average changes in each factory shows that nearly 60% of the total factor production values have increased, while the rest have decreased. Additionally, three factories, namely Zhudong, Douliu, and Liudu, have shown a significant decline, greatly affecting the overall average MPI value.
This study also uses the overall efficiency and total factor productivity of each factory in 2019 as the grouping target. The 15 factories are divided into four groups for explanation. Among them, the operations and maintenance of the Lin-kou, Shi-men, Shi-gang Dam, Hu-wei Liao, and Er-lin factories are good, and their productivity (operational efficiency growth trend) has relatively improved in the past five years. They have advantages in terms of operational and maintenance management systems and can serve as a reference for other factories. The Da-shu, Dou-liu, and Kuang-dai County factories have relatively lower rankings in terms of operational efficiency, and their productivity (operational efficiency growth trend) has shown slow progress or decline in the past five years. It is preliminarily assessed that the three factories have low inflow rates, which hinders the improvement of operational efficiency. It is recommended to accelerate the increase in inflow rates in the future.
In the part of the impact factor, the design of sewage treatment volume, the operating age of the sewage treatment plant, whether the treatment process has denitrification function, location characteristics, and the concentration of COD were tested using the Kruskal-Wallis Test. The results showed that there were statistically significant differences in whether the treatment process has denitrification function and the concentration of COD, while the other three factors showed no statistically significant differences. This means that whether the treatment process of public sewage treatment plants has denitrification function and the initial inflow concentration are key external factors affecting overall efficiency. In order to understand the sensitivity of input items, the overall efficiency changes were calculated when the input values increased or decreased by 10% while keeping the other values unchanged. The results showed that an increase in operating costs would decrease overall efficiency, a decrease in operating costs would affect efficiency, but the trend is not necessarily the same. The load ratio and electricity consumption do not affect overall efficiency.

誌謝 i
中文摘要 ii
ABSTRACT iv
目錄 viii
圖目錄 x
表目錄 xi
第一章 緒論 1
1.1 研究緣起 1
1.2 污水下水道系統現況及困境 2
1.2.1 污水下水道建設執行歷程與現況 2
1.2.2 污水下水道系統維護營運困境 3
1.3 研究動機與目的 4
1.4 研究架構 5
第二章 文獻回顧 7
2.1 公共污水處理廠建設現況 7
2.2 效率評估 12
2.3 邊界分析法 15
2.4 環境相關類別應用資料包絡分析法相關文獻 17
第三章 研究方法 26
3.1 資料包絡分析法模式 26
3.1.1 CCR模式 26
3.1.2 BCC模式 28
3.1.3 麥氏生產力指數分析 30
3.2 研究流程 31
3.2.1 選取投入、產出變項 32
3.2.2 決策單位之界定 33
3.2.3 資料蒐集 34
3.2.4 皮爾森積差相關性分析(Pearson Correlation) 35
3.2.5 影響因子之探討 37
3.2.6 敏感度分析 37
第四章 結果與討論 38
4.1 描述性統計分析 38
4.2 CCR及BCC模式之投入導向分析 40
4.3 差額變數分析 50
4.4 麥氏生產力指數分析 53
4.5 影響因子 58
4.6 敏感性分析 60
第五章 結論與建議 62
5.1 結論 62
5.2 建議 63
附錄A DEA投入及產出項數據 67
附錄B 差額變數值 70
附錄C 麥氏生產力指數 76
附錄D 影響因子分組排名情況 79
附錄E 敏感度分析DEA數值 82

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