To gain insight in the social processes that underlie knowledge sharing in teams, this article questions which team learning behaviors lead to the construction of a shared mental model. Additionally, it explores how the development of shared mental models mediates the relation between team learning behaviors and team effectiveness. Analyses were performed on student-teams engaged in a business simulation game. The measurement of shared mental models was based on cognitive mapping techniques. The results indicate that a team learning perspective provides insight in how people share knowledge. Particularly the team learning behaviors identified as co-construction and constructive conflict are related to the development of shared mental models. In addition, a shared mental model of the task environment in a team leads to improved performance. This underscores the importance of developing shared cognition in teamwork.
This paper assesses the global and sectoral implications of the European Union Biofuels Directive (BFD) in a multi-region computable general equilibrium framework with endogenous determination of land supply. The results show that, without mandatory blending policies or subsidies to stimulate the use of biofuel crops in the petroleum sector, the targets of the BFD will not be met in 2010 and 2020. With a mandatory blending policy, the enhanced demand for biofuel crops has a strong impact on agriculture at the global and European levels. The additional demand from the energy sector leads to an increase in global land use and, ultimately, a decrease in biodiversity. The development, on the other hand, might slow or reverse the long-term process of declining real agricultural prices. Moreover, assuming a further liberalization of the European agricultural market imports of biofuels are expected to increase to more than 50% of the total biofuel demand in Europe. Preface This paper assesses the global and sectoral implications of the European Union Biofuels Directive (BFD) in a multi-region computable general equilibrium framework with endogenous determination of land supply. The results show that, without mandatory blending policies or subsidies to stimulate the use of biofuel crops in the petroleum sector, the targets of the BFD will not be met in 2010 and 2020. With a mandatory blending policy, the enhanced demand for biofuel crops has a strong impact on agriculture at the global and European levels. The additional demand from the energy sector leads to an increase in global land use and, ultimately, a decrease in biodiversity. The development, on the other hand, might slow or reverse the long-term process of declining real agricultural prices. Moreover, assuming a further liberalization of the European agricultural market imports of biofuels are expected to increase to more than 50% of the total biofuel demand in Europe. This work builds forward on the methodlogy developed in the EUruralis project financed by the Dutch Ministry of Agriculture, Nature and Food Quality. In the EUruralis project the GTAP model is extended with first generation biofuels and land markets (Rienks, 2008, Eickhout and Prins 2008). Two methodological improvements which are essential to assess the impact of biofuels and biofuel policies. In this paper we assess the impact of the EU biofuel directive, in another LEI working paper that will appear soon, we assess the the global and sectoral implications of policy initiatives in different countries or regions (e.g. the U.S., the EU, Canada, South Africa or Japan) to enhance bioenergy demand and production.
It is commonly recognized that large uncertainties exist in modelled biofuel-induced indirect land-use change, but until now, spatially explicit quantification of such uncertainties by means of error propagation modelling has never been performed. In this study, we demonstrate a general methodology to stochastically calculate direct and indirect land-use change (dLUC and iLUC) caused by an increasing demand for biofuels, with an integrated economic -land-use change model. We use the global Computable General Equilibrium model MAG-NET, connected to the spatially explicit land-use change model PLUC. We quantify important uncertainties in the modelling chain. Next, dLUC and iLUC projections for Brazil up to 2030 at different spatial scales and the uncertainty herein are assessed. Our results show that cell-based (5 9 5 km 2 ) probabilities of dLUC range from 0 to 0.77, and of iLUC from 0 to 0.43, indicating that it is difficult to project exactly where dLUC and iLUC will occur, with more difficulties for iLUC than for dLUC. At country level, dLUC area can be projected with high certainty, having a coefficient of variation (cv) of only 0.02, while iLUC area is still uncertain, having a cv of 0.72. The latter means that, considering the 95% confidence interval, the iLUC area in Brazil might be 2.4 times as high or as low as the projected mean. Because this confidence interval is so wide that it is likely to straddle any legislation threshold, our opinion is that threshold evaluation for iLUC indicators should not be implemented in legislation. For future studies, we emphasize the need for provision of quantitative uncertainty estimates together with the calculated LUC indicators, to allow users to evaluate the reliability of these indicators and the effects of their uncertainty on the impacts of land-use change, such as greenhouse gas emissions.
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