RESUMO
This research determines the intertemporal relationships caused by the coal, oil, and natural gas consumption in the carbon dioxide emission by the G7 countries from 1965 to 2018. Auto-regressive and Distributed Lags models and Bound test were used to detect cointegration and understand the dynamic effect. Due to structural breaks occurred in the variables, two dummy variables for the periods of breaks, 1978 and 1990 were incorporated respectively. Positive causality was identified, in the sense that the consumption of fossil fuels provides an increase in carbon dioxide emissions. Short-term elasticities indicate that an increase of 1 percentage point in the consumption of oil, coal, and natural gas will cause, respectively, an increase of 0.4823%, 0.3140%, and 0.1717% in carbon dioxide emissions. In the long run, the increase of 1 percentage point in the consumption of oil, coal, and natural gas will cause, respectively, an increase of 0.4924%, 0.2692%, and 0.1829% in carbon dioxide emissions. The error correction model (ECM = -0.4739) indicates that 47.39% of a shock in the carbon dioxide emissions variable is resolved in one year and after 2 years, carbon dioxide emissions return to long term equilibrium.
Assuntos
Dióxido de Carbono , Combustíveis Fósseis , Dióxido de Carbono/análise , Carvão Mineral , Desenvolvimento Econômico , Gás NaturalRESUMO
The impact of air pollution on humans is a worrisome factor that has gained prominence over the years due to the importance of the topic to society. Lung cancer and chronic obstructive pulmonary disease are among the diseases associated with pollution that increase the mortality rate in Brazil and worldwide. Therefore, this study aimed to determine the impacts of air pollutants on mortality rates from chronic obstructive pulmonary disease (COPD) and lung cancer (LC) using vector autoregressive (VAR) modeling. The adjusted model was a VAR(1) and, according to the Granger causality test, the air pollutants selected were PM10, O3, CO, NO2, and SO2. The shocks applied to the variables O3, using the impulse response function, negatively impacted COPD; in the eighth period, which is stabilized. The LC variable suffered more significant variations from O3 and after a shock in this variable, an initially negative response in LC occurred and the series stabilized in period nine. After one year, 20.19% of COPD variance was explained by O3. After twelve months, the atmospheric pollutant O3 represented 5.00% and NO2 represented 4.02% of LC variance. Moreover, the variables that caused the highest impact on COPD and LC mortality rates were O3 and NO2, indicating that air pollution influences the clinical state of people who have these diseases and even contributes to their development. The VAR model was able to identify the air pollutants that have the most significant impact on the diseases analyzed and explained the interrelationship between them.
Assuntos
Poluentes Atmosféricos , Poluição do Ar , Poluentes Ambientais , Neoplasias Pulmonares , Doença Pulmonar Obstrutiva Crônica , Poluentes Atmosféricos/análise , Poluição do Ar/efeitos adversos , Poluição do Ar/análise , Brasil , Humanos , Neoplasias Pulmonares/epidemiologia , Material Particulado/análise , Doença Pulmonar Obstrutiva Crônica/epidemiologiaRESUMO
ABSTRACT: The objective of this research was to forecast the Brazilian national production of agricultural and road machinery in the short term by BOX & JENKINS methodology and determine the persistence effect. Data were obtained at National Association of Automotive Vehicle Manufacturers (ANFAVEA) from January 1960 to October 2019, totaling 718 monthly observations. The Autoregressive Integrated Moving Average (ARIMA) and Autoregressive Conditional Heteroscedasticity (ARCH) methodology were used. The ARIMA (2,1,1)-ARCH (2) model was fitted and persistence of 0.60 was determined, showing that the instability in the series will be for a long period of time.
RESUMO: O objetivo desta pesquisa é prever a produção nacional de máquinas agrícolas e rodoviárias no Brasil, no curto prazo por meio da metodologia BOX & JENKINS e determinar o efeito de persistência na série. Os dados foram obtidos no site da Associação Nacional dos Fabricantes de Veículos Automotores (ANFAVEA) no período de janeiro de 1960 a outubro de 2019, totalizando 718 observações mensais. Os modelos Autoregressivos Integrados e de Médias Móveis (ARIMA) e de Heteroscedasticidade Condicional Autoregressiva (ARCH) foram utilizados para ajustar a média e a variabilidade da série. O modelo ARIMA(2,1,1) - ARCH(2) foi selecionado por meio das estatísticas de ajustes e a persistência determinada foi de 0,60 mostrando que a instabilidade na série é duradoura.