Eugenia Nissi

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.

Residential radon concentration in the Abruzzo region (Italy): a different perspective for identifying radon prone areas

Indoor radon is an important risk factor for human health. Indeed radon inhalation is considered the second cause of lung cancer after smoking. During the last decades, in many countries huge efforts have been made in order to measuring, mapping and predicting radon levels in dwellings. Various researches have been devoted to identify those areas within the country where high radon concentrations are more likely to be found. Data collected through indoor radon surveys have been analysed adopting various statistical approaches, among which hierarchical Bayesian models and geostatistical tools are worth noting. The essential goal of this paper regards the identification of high radon concentration areas (the so-called radon prone areas) in the Abruzzo Region (Italy). In order to accurately pinpoint zones deserving attention for mitigation purpose, we adopt spatial cluster detection techniques, traditionally employed in epidemiology. As a first step, we assume that indoor radon measurements do not arise from a continuous spatial process; thus the geographic locations of dwellings where the radon measurements have been taken can be viewed as a realization of a spatial point process. Following this perspective, we adopt and compare recent cluster detection techniques: the simulated annealing scan statistic, the case event approach based on distance regression on the selection order and the elliptic spatial scan statistic. The analysis includes data collected during surveys carried out by the Regional Agency for the Environment Protection of Abruzzo (ARTA) in 1,861 random sampled dwellings across 277 municipalities of the Abruzzo region. The radon prone areas detected by the selected approaches are provided along with the summary statistics of the methods. Finally, the methodologies considered in this paper are tested on simulated data in order to evaluate their power and the precision of cluster location detection.

The Application of M-Function Analysis to the Geographical Distribution of Earthquake Sequence

Seismicity is a complex phenomenon and its statistical investigation is mainly concerned with the developing of computational models of earthquake processes. However, a substantial number of studies have been performed on the distribution of earthquakes in space and time in order to better understand the earthquake generation process and improve its prediction. The objective of the present paper, is to explore the effectiveness of a variant of Ripley’s K-function, the M-function, as a new means of quantifying the clustering of earthquakes. In particular we test how the positions of epicentres are clustered in space with respect to their attributes values, i.e. the magnitude of the earthquakes. The strength of interaction between events is discussed and results for L’Aquila earthquake sequence are analysed.

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.

Geographically Weighted Regression Analysis of Cardiovascular Diseases: Evidence from Canada Health Data

This paper aims to present an exploratory spatial analysis for ascertaining Canadian regional variations in the relationships between cardiovascular diseases prevalence and some well-established risk factors. Since the geographic variation in risk factors for cardiovascular diseases is too complex to be captured by a single set of regression coefficients, a local regression technique is employed. In particular, in this study, we make use of Geographically Weighted Regression (GWR) models with a ridge regression parameter to condense model complications related to the occurrences of local collinearity in the weighted explanatory variables. Local regression coefficients and associated statistics for both traditional GWR and GWR where a ridge regression parameter has been integrated are compared to evaluate their relative abilities in modelling the heterogeneous impact of risk factors on cardiovascular diseases across space.

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.

A simulation study on the hybrid nature of Tango's index

Since the early 1990s, there has been an increasing interest in statistical methods for detecting global spatial clustering in data sets. Tangos index is one of the most widely used spatial statistics for assessing whether spatially distributed disease rates are independent or clustered. Interestingly, this statistic can be partitioned into the sum of two terms: one term is similar to the usual chi-square statistic, being based on deviation patterns between the observed and expected values, and the other term, similar to Morans I, is able to detect the proximity of similar values. In this paper, we examine this hybrid nature of Tangos index. The goal is to evaluate the possibility of distinguishing the spatial sources of clustering: lack of fit or spatial autocorrelation. To comply with the aims of the work, a simulation study is performed, by which examples of patterns driving the goodness-of-fit and spatial autocorrelation components of the statistic are provided. As for the latter aspect, it is worth noting that inducing spatial association among count data without adding lack of fit is not an easy task. In this respect, the overlapping sums method is adopted. The main findings of the simulation experiment are illustrated and a comparison with a previous research on this topic is also highlighted.

Radon Level in Dwellings and Uranium Content in Soil in the Abruzzo Region: A Preliminary Investigation by Geographically Weighted Regression

Radon is a noble gas coming from the natural decay of uranium. It can migrate from the underlying soil into buildings, where sometimes very high concentration can be found, particularly in the basement or at ground floor. It contributes up to about the 50% of the ionizing radiation dose received by the population, constituting a real health hazard. In this study, we use the geographically weighted regression (GWR) technique to detect spatial non-stationarity of the relationship between indoor radon concentration and the radioactivity content of soil in the Provincia of L’Aquila, in the Abruzzo region (Central Italy). Radon measurements have been taken in a sample of 481 dwellings. Local estimates are obtained and discussed. The significance of the spatial variability in the local parameter estimates is examined by performing a Monte Carlo test.

The Kalman Filter on Three Way Data Matrix for Missing Data: A Case Study on Sea Water Pollution

This paper proposes a method for the reconstruction of missing data in a three-way data array, based on six modified procedures of the optimum Kalman filter in relation to the structural data analysis. The case study regards environmental data on sea water pollution observed in the Adriatic sea.