Previous studies that have attempted to model the participation decision of farmers in agri‐environmental schemes have used a static framework where it was not possible to examine changes in the participation decision of farmers over time. This is rectified in this paper by utilising an 11‐year panel that contains information on 300 farmers for each year. The structure of this dataset allows us to employ discrete time duration random effects panel data logit models to model the determinants of entering the Irish Rural Environment Protection Scheme (REPS). We introduce a dynamic element into a number of the models by using the random effects logit model estimator, with lagged dependent variables as additional explanatory variables. The results point to the fact that systems of farming that are more extensive and less environmentally degrading remain those most likely to participate in the REPS. In addition, the results highlight the fact that where no attempt is made to control for unobserved heterogeneity or path dependency the effects of the farm‐ and farmer‐specific characteristics may be overestimated.
Deep learning-based multi-view stereo has emerged as a powerful paradigm for reconstructing the complete geometrically-detailed objects from multi-views. Most of the existing approaches only estimate the pixel-wise depth value by minimizing the gap between the predicted point and the intersection of ray and surface, which usually ignore the surface topology. It is essential to the textureless regions and surface boundary that cannot be properly reconstructed. To address this issue, we suggest to take advantage of point-to-surface distance so that the model is able to perceive a wider range of surfaces. To this end, we predict the distance volume from cost volume to estimate the signed distance of points around the surface. Our proposed RA-MVSNet is patch-awared, since the perception range is enhanced by associating hypothetical planes with a patch of surface. Therefore, it could increase the completion of textureless regions and reduce the outliers at the boundary. Moreover, the mesh topologies with fine details can be generated by the introduced distance volume. Comparing to the conventional deep learning-based multiview stereo methods, our proposed RA-MVSNet approach obtains more complete reconstruction results by taking advantage of signed distance supervision. The experiments on both the DTU and Tanks & Temples datasets demonstrate that our proposed approach achieves the state-of-the-art results.
We adapt the standard random utility model to take account of the heterogeneity of recreational preferences by using what we call a “skilled-based conditional logit framework”. By separating out our sample of whitewater kayakers into two exogenously identifiable groups (based on their skill level) and running separate conditional logits for each group we are able to take account of the fact that kayakers of different skill levels are looking for different characteristics from the whitewater site they choose to visit. We find that not taking into account the differences in the skill of the kayakers and the grade of the river will result in an overestimation of the welfare estimates associated with improvements to lower grade whitewater sites (which are frequented by basic/intermediated proficiency level kayakers) and underestimating welfare estimates associated with changes in the attributes of higher grade whitewater sites (which are frequented by advanced proficiency level kayakers). Copyright Springer Science+Business Media, Inc. 2007preference heterogeneity, Random Utility Model, skill levels, whitewater kayaking, Q51 Q56,
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