廢棄物清運系統績效評估與資源分配模式之建立與應用

Abstract

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.