ABSTRACT

This paper describes an approach to measure the intensity and the territorial dispersion of spatial proximity effects, which affect efficiency scores obtained through data envelopment analysis in the field of municipal waste management systems (MWMSs). In particular, we show that these analyses cannot be conducted by relying on efficiency scores that are not comparable over time and treated as cross-sectional data, as is done in most previous studies. Instead, the use of panel data is a key element to obtain reliable results. We used a meta-frontier approach to obtain meta-efficiency scores comparable over time and a modified conditional autoregressive (CAR) model to provide an estimation of the intensity of spatial proximity effects. This approach was applied to data on 277 MWMSs located in the Italian region of Abruzzo. Our method provides useful information for policymakers. In particular, the areas in which stagnating and suboptimal performance can be expected over time can be identified by plotting over the regional territory the posterior medians of the random effects obtained by the spatial component of the CAR model together with the highly efficient municipalities. To improve efficiency, these areas require an active intervention by levels of government higher than the municipal level.

摘要

本文描述了一种测量空间邻近效应的强度和地域分散的方法，这些效应会影响通过城市废物管理系统 (MWMS) 领域的数据包络分析获得的效率得分。特别是，我们表明，这些分析不能像以前的大多数研究那样，依赖于随着时间推移不可比较并被视为横断面数据的效率得分来进行。相反，使用面板数据是获得可靠结果的关键要素。我们使用元前沿方法来获得随时间可比的元效率分数，并使用改进的条件自回归 (CAR) 模型来估计空间邻近效应的强度。这种方法适用于位于意大利阿布鲁佐地区的 277 个 MWMS 的数据。我们的方法为政策制定者提供了有用的信息。特别是，可以通过在区域范围内绘制由 CAR 模型的空间组件与高效市政当局一起获得的随机效应的后验中值，来识别随着时间推移可能出现停滞和次优性能的区域。为了提高效率，这些领域需要市级以上各级政府的积极干预。