8 resultados para Multi-criteria decision analysis (MCDA)

em Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco


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Managing protected areas implies dealing with complex social-ecological systems where multiple dimensions (social, institutional, economic and ecological) interact over time for the delivery of ecosystem services. Uni-dimensional and top-down management approaches have been unable to capture this complexity. Instead, new integrated approaches that acknowledge the diversity of social actors in the decision making process are required. In this paper we put forward a novel participatory assessment approach which integrates multiple methodologies to reflect different value articulating institutions in the case of a Natura 2000 network site in the Basque Country. It integrates within a social multi-criteria evaluation framework, both the economic values of ecosystem services through a choice experiment model and ecological values by means of a spatial bio-geographic assessment. By capturing confronting social and institutional conflicts in protected areas the participatory integrated assessment approach presented here can help decision makers for better planning and managing Natura 2000 sites.

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DOI del libro: http://dx.doi.org/10.5772/1399

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[ES] El objetivo del artículo es explorar la potencialidad de la Evaluación Multi-criterio Social como enfoque metodológico para evaluar Espacios Naturales Protegidos (ENP) e iniciar un proceso de discusión sobre la conveniencia de incorporar esta metodología para la gobernanza de la Red Natura 2000 de la Comunidad Autónoma de Euskadi. Del caso de estudio analizado se desprende que la inclusión de la población local en un proceso participativo dirigido a la planificación y manejo de los ENP contribuiría a reducir el número y calado de conflictos potenciales. Asimismo, facilitaría la gobernanza efectiva de los ENP, lo que parece un reto ineludible para que las áreas protegidas logren sus objetivos y funciones.

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Background: Consensus development techniques were used in the late 1980s to create explicit criteria for the appropriateness of cataract extraction. We developed a new appropriateness of indications tool for cataract following the RAND method. We tested the validity of our panel results. Methods: Criteria were developed using a modified Delphi panel judgment process. A panel of 12 ophthalmologists was assembled. Ratings were analyzed regarding the level of agreement among panelists. We studied the influence of all variables on the final panel score using linear and logistic regression models. The explicit criteria developed were summarized by classification and regression tree analysis. Results: Of the 765 indications evaluated by the main panel in the second round, 32.9% were found appropriate, 30.1% uncertain, and 37% inappropriate. Agreement was found in 53% of the indications and disagreement in 0.9%. Seven variables were considered to create the indications and divided into three groups: simple cataract, with diabetic retinopathy, or with other ocular pathologies. The preoperative visual acuity in the cataractous eye and visual function were the variables that best explained the panel scoring. The panel results were synthesized and presented in three decision trees. Misclassification error in the decision trees, as compared with the panel original criteria, was 5.3%. Conclusion: The parameters tested showed acceptable validity for an evaluation tool. These results support the use of this indication algorithm as a screening tool for assessing the appropriateness of cataract extraction in field studies and for the development of practice guidelines.

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In multisource industrial scenarios (MSIS) coexist NOAA generating activities with other productive sources of airborne particles, such as parallel processes of manufacturing or electrical and diesel machinery. A distinctive characteristic of MSIS is the spatially complex distribution of aerosol sources, as well as their potential differences in dynamics, due to the feasibility of multi-task configuration at a given time. Thus, the background signal is expected to challenge the aerosol analyzers at a probably wide range of concentrations and size distributions, depending of the multisource configuration at a given time. Monitoring and prediction by using statistical analysis of time series captured by on-line particle analyzers in industrial scenarios, have been proven to be feasible in predicting PNC evolution provided a given quality of net signals (difference between signal at source and background). However the analysis and modelling of non-consistent time series, influenced by low levels of SNR (Signal-Noise Ratio) could build a misleading basis for decision making. In this context, this work explores the use of stochastic models based on ARIMA methodology to monitor and predict exposure values (PNC). The study was carried out in a MSIS where an case study focused on the manufacture of perforated tablets of nano-TiO2 by cold pressing was performed