RESUMO
This paper attempts to model both static and dynamic dependence structures and measure impacts of energy consumptions (both renewable (EC) and non-renewable (REN) energies), economic globalization (GLO), and economic growth (GDP) on carbon dioxide (CO2) emissions in Argentina over the period 1970-2020. For analyses purpose, the current research deploys the novel static and dynamic copula-based ARIMA-fGARCH with different submodels. The static bivariate copula results show that the growth rates of the pairs EC-CO2 and GDP-CO2 are asymmetrically positive co-movements and have high left tail (extreme) dependencies, implying that the increase in non-renewable energy and economic growth can critically contribute to the environmental degradation, and the decrease in the consumption of non-renewable energy at a high level will consequently reduce the CO2 emissions at the same level. Based on several copula-based dependence measures, we document that between the two factors, the non-renewable energy has a stronger impact than the economic growth regarding the CO2 emissions. On the other hand, the growth rates of both economic globalization and renewable energy symmetrically negatively co-move with the growth rates of the CO2 emissions, but they have no extreme dependencies, indicating that these factors contribute to Argentina's environmental quality, in which the factor of renewable energy has a greater impact. Furthermore, the dynamic copula outcomes show that the (tail) dependencies of CO2 emissions on the non-renewable energy and economic growth are time-varying, while the pairs REN-CO2 and GLO-CO2 possess only dynamic dependencies, but no dynamic tail dependencies. Moreover, through the dynamic copula-based dependence, the environmental Kuznets curve (EKC) hypothesis can be estimated and illustrated explicitly. In addition, we leverage multivariate vine copulas for modelling dependence structures of the five variables simultaneously, which can reveal rich information regarding conditional associations among the relevant variables. Some policy implications are also provided to mitigate CO2 emissions.
Assuntos
Dióxido de Carbono , Desenvolvimento Econômico , Argentina , Dióxido de Carbono/análise , Internacionalidade , PolíticasRESUMO
O mapeamento de variáveis climáticas, como chuvas intensas, é de fundamental importância para o manejo ambiental. Para isto, ferramentas estatísticas para interpolação espacial devem ser devidamente analisadas e caracterizadas. Assim, objetivou-se com este trabalho analisar modelos e métodos de modelagem do semivariograma que melhor se ajustem a chuvas intensas com duração de 20, 60, 360 e 1440 minutos e tempos de retorno de 5, 50 e 100 anos, fornecendo subsídios primordiais para espacialização da mesma pelo interpolador geoestatístico, para o Estado de Minas Gerais. Foram testados os modelos esférico, exponencial e gaussiano pelos métodos de ajuste da Máxima Verossimilhança (MV) e Mínimos Quadrados Ponderados (MQP). Utilizou-se como critério de escolha do melhor modelo, o menor erro médio gerado pela validação cruzada, e em caso de similaridade, também foram considerados o maior grau de dependência espacial e o menor efeito pepita, além da análise visual do ajuste do modelo ao semivariograma experimental. O modelo exponencial se sobressaiu em nove das doze situações analisadas, o gaussiano em duas e o esférico em uma situação. Quanto aos métodos de ajuste, o MQP sobressaiu em todos os casos estudados, o que permite sugerir o modelo exponencial ajustado pelo método dos mínimos quadrados ponderados como sendo o mais adequado para o mapeamento da chuva intensa para as condições do Estado de Minas Gerais.
Climate variables mapping, as intense rainfall, is very important to environmental management. Although, statistical tools for spatial interpolation should be analyzed and characterized. This paper aims to analyze models and methods of semi-variogram modeling applied to intense rainfall with duration time of 20, 60, 360 and 1440 minutes and 5, 50 and 100 years of recurrence and consequently, giving support for its mapping, using kriging, in Minas Gerais State. Exponential, Spherical and Gaussian semi-variogram models were tested based on Weighted Minimum Square (WMS) and Maximum Likelihood (ML) methods, using GeoR software. For the best model and method evaluation was considered the mean absolute error produced by cross-validation. For mean error similarity, it was considered the spatial degree of dependence and smaller nugget effect. Visual adjustment of semi-variogram was also analyzed to complete the selection. Exponential model was predominant in nine of twelve situations, followed by Gaussian model in two situations and Spherical for just one. Weighted Minimum Square was the best adjust method in all situations. These results have indicated the exponential model adjusted by Weighted Minimum Square to intense rainfall mapping for Minas Gerais State conditions.