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Efficiency and sustainability indicators for passenger and commodities transportation systems:The case of Siena, Italy

Ecological Indicators
Publication Date
DOI: 10.1016/s1470-160x(03)00040-2
  • Transports
  • Energy
  • Exergy
  • Emergy
  • Environmental Indicators
  • Ecology
  • Economics
  • Physics


Abstract Three different energy analysis approaches (energy and embodied energy, exergy and emergy analysis) have been applied to the road and railway systems of a medium size district of central Italy, in order to shed light on the dynamics of the local transport sector and develop a tool for analysis capable of taking the system complexity into account. Road and railway systems, respectively, support passenger flows of 3.57E9 p-km (passengers per km) per year and 0.17E9 p-km per year and commodity flows of 2.5E9 t-km (tonnes per km) per year and 0.35E9 t-km per year, generating a total energy consumption equal to 1.84E5 tonnes of oil equivalent per year. The passenger mass transport on road (buses) shows globally the best performance among the patterns investigated, while railway ranks higher for commodity transport, according to most of the calculated intensity indicators. Several improvement options are also evaluated on the basis of the first- and second-order exergy efficiency. Some of the suggested improvements, even showing high theoretical possibility, do not match the transport needs of the investigated area, as indicated by their huge material and emergy intensities (measures of ecological footprints) even if it cannot be excluded that they may appear more appropriate to nationwide transportation patterns. In conclusion, although data and indicators refer to a well identified region under specific geographic and socio-economic conditions, results suggest that a complex system such as transport is very unlikely to be described by a linear relation between input resource and output service delivered. Even when thermodynamically based approaches are properly used to describe the system behavior, findings very often do not converge, and require that different indicators are compared to yield a comprehensive picture of the system dynamics. An integrated approach is therefore suggested to support decision making in the presence of diverging results.

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