臺灣博碩士論文加值系統

廢棄物清運向來佔廢棄物管理總經費最大比例，有必要評估及提昇其績效。
Data Envelopment Analysis 法 (DEA）雖常用以評估環境績效，唯其計算效率時
會選擇最有利的指標權重令效率值最大，易產生僅少數指標表現較佳即可得到較
高效率值。此外，各地區人口密度不同所造成的空間性差異會影響評量結果。而
資源分配亦是影響清運績效的重要因素之一，唯現今的資源分配方式並未考量此
特性，本研究因而發展可改善廢棄物清運績效評估及資源分配方法。
本研究將以全台各鄉鎮市清潔隊為案例建置績效評估與資源分配之方法。為
改善DEA 法會受少數表現好的指標影響評估結果，發展評估無效率之Reverse
DEA (RDEA)法來識別出相對無效率之受評量單位，且據其評估結果結合有效率
評估方法建立一套較嚴僅之Inefficiency Countervailed DEA (IC-DEA)法，並用以
分析企業環境績效作為驗證及示範。惟廢棄物清運指標架構較複雜，且評估績效
時亦須考量資源負荷的情況，故本研究進而發展 Enhanced IC-DEA (EIC-DEA)
法，且分析用以評估廢棄物清運績效的可行性。由於EIC-DEA 法仍以不同權重
評估，且空間性差異是影響清運績效評估的主因之一，本研究因而建立Spatial
Inefficiency Countervailed Common Weight (SIC-CW)法以求取適當的共同權重，
以期同時改善無效率補償及空間性問題。此外，適當的資源分配可有效提高清運
績效，本研究因此運用對偶模式建立MSW Resource Usage Analysis (MSW-RUA)
法，以期提高資源善用率。研究結果顯示，加入過程指標可考量工作負荷並改善
過去的評估方法，同時，考量空間差異性與共同權重可調整各清潔隊因空間分佈
不同所造成之服務表現不同之情況，並可提供較利於實務使用之權重組合。此
外，研究結果亦可提供提昇資源使用效率所需改善之資源配置，作為後續規劃之
參考，所發展方法可有效改善績效評估及資源分配。

Municipal solid waste (MSW) collection is significant components of waste
management; consequently, assessment methods for MSW collection performance warrant
evaluation. Data envelopment analysis (DEA), a method frequently used for performance
assessment, assigns the most advantage weight set to maximize the performance value of an
evaluated unit. However, high values in a few indicators can lead to a unit being regarded
as ‘efficient,’ despite valuing poorly in other essential indicators. Furthermore, spatial
differences resulting from varying population densities between regions influence the
practicality of assessments. Distribution of resources also plays an important role in the
effectiveness of MSW collection, but prior resource distribution methods have yet to take
this into account. Therefore, this study has developed methods to improve MSW collection
performance evaluation and resource usage.
This study developed methods to assess MSWC services and resource allocation for
307 Taiwan local governments. For overcoming the drawback of the DEA method,,
Reverse Data Envelopment Analysis (RDEA) was established to discern the relative
significance of indicators. DEA and RDEA models were then combined in this study to
develop a more precise method of analysis, referred to here as the Inefficiency
Countervailed Data Envelopment Analysis (IC-DEA), which can be verified through
evaluation of corporate environmental performance. The indicator framework of MSW
collection evaluation is more complex than IC-DEA case; therefore, when assessing
performance, work-loading must also be considered. This study developed the Enhanced
Inefficiency Countervailed Data Envelopment Analysis (EIC-DEA) method to analyze the
MSW collection performance. Because the different weight sets of EIC-DEA results and
spatial issue heavily influence MSW collection performance, the Spatial Inefficiency
Countervailed Common Weight (SIC-CW) method was enhanced to obtain suitable
common weight, addressing the issue of ineffective compensation and differences in spatial
distribution. Additionally, appropriate resource usage can increase the effectiveness of
MSW collection. Consequently, a dual mode approach was employed to create the MSW
Resource Usage Analysis (MSW-RUA) method, which increases resource efficiency.
Additional procedure indicators with work loading using these methods resulted in
improvements over previous approaches for evaluation. Considerations of spatial
differences and common weighting can also adjust for differences in performance caused
by disparities in spatial distribution, and provide a more practical weighting structure.
Results from this study can be used as references to determine improvements necessary for
resource efficiency as well as follow-up planning. Approaches of analysis proposed in the
study can be used to improve MSW collection performance assessment and resource usage.

中文摘要 i
英文摘要 ii
致謝 iii
目錄 iv
表目錄 vii
圖目錄 viii
符號說明 xi
第一章 前言
1.1 研究緣起 1
1.2 研究目的 6
1.3 論文內容 7
第二章 文獻回顧
2.1 清運績效評估及資源使用與分配 8
2.2 資料包絡分析法(DEA) 10
2.3 空間性分析 12
2.4 權重限制與共同權重 13
第三章 研究方法
3.1 研究架構與流程 16
3.2 IC-DEA法之建立與示範-企業環境績效評估 22
3.2.1 DEA 模式 22
3.2.2 RDEA 模式 23
3.2.3 IC-DEA績效評估 24
3.2.4 IC-DEA法應用驗證－企業環境績效評估 25
3.2.4.1 DEA模式－CEP績效評估值結果 26
3.2.4.2　RDEA模式－CEP績效評估值結果 28
3.2.4.3　IC-DEA績效評估-CEP績效評估值結果 29
3.3 以EIC-DEA法改善評估廢棄物清運績效評估 32
3.3.1 廢棄物績效評估指標 32
3.3.2 DEA模式 34
3.3.3 RDEA模式 36
3.3.4 EIC-DEA績效評估 39
3.4 以SIC-CW法改善廢棄物清運績效評估之空間性差異 40
3.4.1 空間性因子與指標之篩選 40
3.4.2 E-CW模式 42
3.4.3 S-CW模式 44
3.4.4 EI-CW模式 46
3.4.5 SI-CW模式 48
3.4.6 EIC-CW績效評估 50
3.4.7 SIC-CW績效評估 50
3.5 MSW-RUA廢棄物清運資源分配法 51
3.5.1 資源分配設置基準 51
3.5.2 空間性分配指標篩選與建立與特殊地區分析 51
3.5.3 MSW-RUA模式 53
第四章 結果與討論
4.1 以EIC-DEA法評估廢棄物清運績效結果分析 56
4.1.1 DEA模式結果 56
4.1.2 RDEA模式結果 62
4.1.3 EIC-DEA績效評估結果分析 68
4.2 以S-CW法評估廢棄物清運績效結果分析 74
4.2.1 共同權重建立與空間性因子篩選結果 74
4.2.2 E-CW與S-CW模式結果 74
4.2.3 EI-CW與SI-CW模式結果 80
4.2.4 EIC-CW與SIC-CW績效評估結果分析 86
4.3 MSW-RUA法應用結果分析 91
4.3.1 清運資源空間性因子篩選結果 91
4.3.2 MSW-RUA評估結果分析 91
第五章 結論與建議
5.1 結論 98
5.2 建議 100
參考文獻 102
附錄一 108
附錄二 125
附錄三 141
附錄四 150

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