9 resultados para Particle pollution

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


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Revised 2008-08.-- Published as an article in: Journal of Public Economic Theory (2008), 10(4), 563-594.

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This paper studies the macroeconomic effects of a permanent increase in foreign aid in a model that takes into account environmental quality. We develop a dynamic equilibrium model in which both public investment in infrastructure and environmental protection can be financed using domestic resources and international aid programs. The framework considers four scenarios for international aid: untied aid,aid fully tied to infrastructure, aid fully tied to abatement, and aid equally tied to both types of expenditures. We find that the effects of the transfers may depend on (i) the structural characteristics of the recipient country (the elasticity of substitution in production and its dependence on environment and natural resources) and on (ii) how recipient countries distribute their public expenditure. These results underscore the importance of these factors when deciding how and to what extent to tie aid to infrastructure and/or pollution abatement.

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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