Environmental sustainability in superior education: case study of Lisbon College of Health Technology

Over the centuries there has been a growing trend of societies and it is possible to verify their economic growth. This growth has provided an increased pressure on natural resources, often over-reaching the boundaries of each country, which has called into question the level of environmental sustainability in different countries. Sustainability is understood as a complex concept involving ecological, social, economic dimensions and temporal urban processes. Therefore, Firmino (2009) suggests that the ecological footprint (EF) allows people to establish dependency relations between human activities and the natural resources required for such activities and for the absorption of waste generated. According to Bergh & Verbruggen (1999) the EF is an objective, impartial and one-dimensional indicator that enables people to assess the sustainability. The Superior Schools have a crucial role in building the vision of a sustainable future as a reality, because in transmitting values and environmental principles to his students, are providing that they, in exercising his professional activity, make decisions weighing the environmental values. This ensures improved quality of life. The present study aims to determine the level of environmental sustainability of the Academic Community of Lisbon College of Health Technology (ESTeSL), by calculating the EF, and describe whether a relation between Footprint and various socio-demographic characteristics of the subjects.

Ecological footprint as an indicator of sustainability at Lisbon School of Health Technology, Portugal

Higher education institutions, has an active role in the development of a sustainable future and for this reason, it is essential that they became environmentally sustainable institutions, applying methods such as the Ecological Footprint analysis. This study intent is to strengthen the potential of the ecological footprint as an indicator of the sustainability of students of Lisbon School of Health Technology, and identify the relationship between the ecological footprint and the different socio-demographic variables.

The role of teaching decision analysis for sustainability in engeneering schools

This paper addresses the role that decision analysis plays in helping engineers to gain a greater understanding of the problems they face. The need of structured decision analysis is highlighted as well as the use of multiple criteria decision analysis to tackle sustainability issues with emphasis in the use of MACBETH approach. Some insights from a Portuguese Summer Course on engineering for sustainable development are presented namely the students 'and teacher perceptions about the module of decision analysis for sustainability.

Supply chain optimization of residual forestry biomass for bioenergy production: the case study of Portugal

Within a large set of renewable energies being explored to tackle energy sourcing problems, bioenergy can represent an attractive solution if effectively managed. The supply chain design supported by mathematical programming can be used as a decision support tool to the successful bioenergy production systems establishment. This strategic decision problem is addressed in this paper where we intent to study the design of the residual forestry biomass to bioelectricity production in the Portuguese context. In order to contribute to attain better solutions a mixed integer linear programming (MILP) model is developed and applied in order to optimize the design and planning of the bioenergy supply chain. While minimizing the total supply chain cost the production energy facilities capacity and location are defined. The model also includes the optimal selection of biomass amounts and sources, the transportation modes selection, and links that must be established for biomass transportation and products delivers to markets. Results illustrate the positive contribution of the mathematical programming approach to achieve viable economic solutions. Sensitivity analysis on the most uncertain parameters was performed: biomass availability, transportation costs, fixed operating costs and investment costs. (C) 2015 Elsevier Ltd. All rights reserved.