A macro-scale methodology for vehicle emissions estimation is described. The methodology is based on both correlations between activity level and PM, CO, THC and NO x vehicle emissions and relationships between demographic and socioeconomic variables and transportation activity level. First, pollutant emissions were correlated with transportation activity, expressed as vehicle-km/year, using existing data collected from mobile sources emission inventories in nine urban cities of Chile. Second, demographic and socio-economic variables were pre-selected from those that could intuitively be correlated with vehicle activity level and considering the data availability. Using the individual R 2 correlation coefficient as variable selection criterion, population, the number of vehicles, fuel consumption, gross domestic product, average family incomes and road kilometers were finally chosen. A different set of explicative variables was considered for different vehicle categories, based on the selection criterion above mentioned. Then, correlation functions between these variables and transport activity were obtained by non-linear Gauss-Newton least square method. This methodology was applied to eighteen provinces of the country obtaining total annual emission for mobile sources, divided into six main vehicles categories.
La clasificación y predicción de quiebra de empresas es un tema ampliamente tratado en el ámbito internacional, sin embargo existen pocos estudios de este tipo aplicados a las empresas chilenas. En este contexto, el objetivo de esta investigación es identificar cuál es el modelo que clasifica y predice, con mayor grado de confiabilidad, la quiebra de empresas en Chile. Con tal fin, se comparan tres modelos comúnmente utilizados: Análisis Discriminante Múltiple (ADM), Regresión Logística (LOGIT) y Redes Neuronales (RN), los que utilizan diferentes índices financieros, variables macroeconómicas y otras variables de control. Los modelos fueron aplicados a una muestra de 98 empresas, seleccionadas accidentalmente, sin restricción de giro comercial, 49 quebradas y 49 no quebradas. El resultado de la investigación muestra que si bien el modelo de Redes Neuronales resultó superior, tanto al modelo ADM como al LOGIT, en lo que respecta a clasificación y predicción, se requiere de otras herramientas para determinar el conjunto óptimo de variables a utilizar.
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