Agnese Rapposelli

Monitoring environmental efficiency: An application to Italian provinces

With growing environmental legislation and mounting popular concern for the environment and the quality of life, there has been an increasing recognition in developed nations of the importance of good environmental performance, to assure the reduction of environmental disamenities (such as pollutant emissions and waste) generated as outputs of the production of goods and services. For this reason the impact of all kinds of organisations on environment needs to be identified. The main objective of this empirical study is to evaluate the environmental efficiency of Italian provinces for the year 2004 by using the non-parametric approach to efficiency measurement, represented by Data Envelopment Analysis technique. This method has to be modified to the context of environmental performance, considering three kinds of variables: inputs, outputs and undesirable outputs. In order to rate the performance of Italian provinces, we therefore propose a variant of this linear programming methodology and we extend the analysis to include the presence of environmental harms.

Analysing Industrial Accidents in European Countries Using Data Envelopment Analysis

There has been increasing interest in improving working conditions and in reducing occupational accidents and diseases in the European Union. This paper examines the performance of fifteen European countries, in respect to this issue, in three economic sectors - manufacturing, construction and distribution trades - by means of the non-parametric approach to efficiency measurement, represented by Data Envelopment Analysis (DEA). A linear programming framework is therefore used to construct a production frontier which allows measurement of relative efficiency among national institutions in the sample considered.

Do Agglomeration Externalities Enhance Regional Performances in Production Process? A Stochastic Frontier Approach

The aim of the present work is to estimate an aggregate production function for the 20 Italian regions by emphasizing the role that agglomeration externalities (localization externalities and urbanization externalities) and spatial spillovers have in influencing the technical efficiency of the production process. To this purpose, we use the stochastic frontier approach. The results highlight the relevance and the positive impact that localization and urbanization externalities have in improving the efficiency level of the production process of Northern and Central Italian regions. Furthermore, spatial spillovers represent a source of development and growth for Northern, Central, and Southern regions. In particular, after considering spatial spillovers, we observe a reduction of the concentration and an increase of the diffusion process of efficiency among Italian regions. For some Northern and Central regions, we can observe that there are some richer regions and some poorer regions with regard to their capability to benefit from spatial spillovers.

Performance Measurement of Italian Provinces in the Presence of Environmental Goals

The widespread of sustainable development concept intimates a vision of an ecologically balanced society, where it is necessary to preserve environmental resources and integrate economics and environment in decision-making. Consequently, there has been increasing recognition in developed nations of the importance of good environmental performance, in terms of reducing environmental disamenities, generated as outputs of the production processes, and increasing environmental benefits. In this context, the aim of the present work is to evaluate the environmental efficiency of Italian provinces by using the non-parametric approach to efficiency measurement, represented by Data Envelopment Analysis (DEA) technique. To this purpose, we propose a two-step methodology allowing for improving the discriminatory power of DEA in the presence of heterogeneity of the sample. In the first phase, provinces are classified into groups of similar characteristics. Then, efficiency measures are computed for each cluster.