Freight transport in the context of industrial ecology and sustainability: evaluation of uni- and multi-modality scenarios via life cycle assessment

Purpose

This study aims at comparing, from an environmental point of view, four different scenarios of freight transport at the Italian level, on an equal base of route between supplier and customer. The first scenario included freight movements by truck and mainly ship, the second included track and mainly train, the third was the three-modal based scenario, whilst the fourth scenario was the only uni-modal, based only upon truck movement.

The study was conducted to find the environmentally sustainable solution, or at least a sustainable trade-off, as well as the most environmentally burdening issues, associated with the geographic dimension of transport in Italy, towards sustainability.

Methods

Using uni‐ and multi-modal freight movements by truck, rail and ship, a life cycle assessment (LCA) was developed to estimate the related environmental burdens both at the midpoint and at the endpoint levels from the consumption of primary energy and natural resources along with the emissions of greenhouse gases (GHGs) and of other pollutants. Primary data were compiled as part of the inventory analysis and consisted in the transport flows associated with the system investigated: those were calculated from the distance travelled and the goods load transported. Primary data were then combined with secondary data that were modelled with the transport life cycle modules contained in Ecoinvent: from those modules, the fuel consumption amounts associated transport flows were extrapolated, and used for the assessment.

Results

Results showed that the environmental impact of the multi-modal scenarios is lower compared with the uni-modal scenario. The best performing option was found to be the third scenario providing use of all the three freight means, namely ship, train and truck. However, this scenario is not being practiced for several reasons, mainly due to control and monitoring difficulties of each step and higher operational costs. The first and second scenarios showed a quite comparable environmental behaviour and so are to be considered as viable options.

Conclusions

Apart from highlighting the most environmentally viable transport options, the study contributed to finding the indicators of environmental impact and damage that best describe the system investigated and are recommended by this author team to be accounted for in future assessments in the transport sector. Finally, although site-specific, the results of this study may be useful to logistics companies, policy and decision makers of other regions and countries towards identifying and promoting environmentally optimal freight transport solutions, contributing to sustainability of the transport sector.

     

A transformation‐based approach to Gaussian mixture density estimation for bounded data

Finite mixture of Gaussian distributions provide a flexible semiparametric methodology for density estimation when the continuous variables under investigation have no boundaries. However, in practical applications, variables may be partially bounded (e.g., taking nonnegative values) or completely bounded (e.g., taking values in the unit interval). In this case, the standard Gaussian finite mixture model assigns nonzero densities to any possible values, even to those outside the ranges where the variables are defined, hence resulting in potentially severe bias. In this paper, we propose a transformation‐based approach for Gaussian mixture modeling in case of bounded variables. The basic idea is to carry out density estimation not on the original data but on appropriately transformed data. Then, the density for the original data can be obtained by a change of variables. Both the transformation parameters and the parameters of the Gaussian mixture are jointly estimated by the expectation‐maximization (EM) algorithm. The methodology for partially and completely bounded data is illustrated using both simulated data and real data applications.