A considerable agreement exists about the importance of promoting entrepreneurship to stimulate economic development and employment generation. In particular, entrepreneurship education has been considered one of the key instruments to increase the entrepreneurial attitudes of both potential and nascent entrepreneurs. Nevertheless, the factors that determine the individual's decision to start a venture are still not completely clear. Cognitive approaches have attracted considerable interest recently. But the explaining capacity of personality traits or demographic characteristics is still considered. Therefore, there is a need to clarify which elements play the most influential role in shaping the personal decision to start a firm. This paper tries to contribute to filling this gap by providing empiricallybased suggestions for the design of improved entrepreneurship education initiatives. The empirical analysis is based on two essential elements: firstly, an already validated instrument (EIQ); secondly, a statistical method (factor-regression procedure) which is not dependent on any theoretical approach. It uses all the information collected through the questionnaire items, selecting them solely based on their capacity to explain the dependent variable. Results will allow the design of more effective education initiatives. They suggest that personal attitude and perceived behavioural control are the most relevant factors explaining entrepreneur- Int Entrep Manag J (2011) 7:195-218 ial intentions. Thus, based on these results, a number of considerations about the most effective role of education in promoting and developing attitudes and intentions towards entrepreneurship are considered. Besides, the EIQ could be used as an evaluation instrument for entrepreneurial education programmes.

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In this paper we explore the use of two different global multiregional input-output databases (GTAP-MRIO and WIOD) for the calculation of the global carbon emissions embodied in the final demand of nations (carbon footprint). We start our analysis with a description of the main characteristics of the databases and comparing their main components. Then, we calculate the carbon footprint with both databases and identify (from a global perspective) the most relevant factors underlying the resulting differences using structural decomposition analysis. The main conclusion that can be drawn is that, on average, certain elements of both databases can be said to be similar in around 75% to 80%, being only a few elements in each table the main drivers of the major differences. The divergences in the datasets of four countries explain almost 50% of the differences in the carbon footprint (USA 19.7%, China 18.1%, Russia 6.4% and India 4.3%). Industry wise, 50% of the differences can be explained by the divergences in three industries: (electricity 32.7%, refining 9.9% and inland transport 7.1%).