RESUMO
Renewable sources are relevant in a country's energy planning because they are linked to the creation of opportunities for technological, economic, and productive development guided by the principles of sustainability. Thus, the aim of this study was to investigate the relation between electric generation capacity by renewable and non-renewable energies and Brazilian socioeconomic variables. The analysis of the interrelationships between electricity generation capacity and economic growth in Brazil, from April 2009 to March 2017, was carried out by the vector autoregressive and autoregressive distributed lag methodologies. It was verified that the variance of employment is explained by renewable sources: hydroelectric in 7.71%, biomass in 1.99%, wind energy in 3.13%, and solar energy in 10.58%. While, the GDP variance is explained in 3.15% by hydroelectric energy, 0.06% by biomass, 1.70% by wind energy, and 17.38% by solar energy. The export variance is explained by renewable sources: hydroelectric 2.48%, biomass 0.39%, wind energy 2.34%, and solar energy 17.58%. Finally, the variance of the minimum wage is explained by hydroelectric energy in 1.48%, biomass in 5.09%, wind energy in 9.09%, and solar energy in 10.67%. An ARDL (1, 1, 2, 0, 0, 0, 3, 2, 0, 2, 0, 2) model was also adjusted for natural gas, with AIC (13.082) and BIC (13.739), and the ARDL (1, 0, 1, 0, 0, 0, 0, 0, 3, 0, 0, 4) model adjusted for hydroelectric power, with AIC (13.633) and BIC (14.189), considering the variables' order cited above. Through the adjustment of the ARDL model, it was verified that there is a long-term influence of socioeconomic variables on electricity production variables, both renewable and non-renewable ones. The analysis of the impulse response function and the variance decomposition allowed us to verify that the installed capacity for production of electric energy exerts influence on Brazilian socioeconomic variables considered in this study.
Assuntos
Desenvolvimento Econômico , Energia Renovável , Biomassa , Brasil , Dióxido de Carbono/análise , Eletricidade , Gás Natural , Energia Solar , VentoRESUMO
Tropical forests play a critical role in carbon and water cycles at a global scale. Rapid climate change is anticipated in tropical regions over the coming decades and, under a warmer and drier climate, tropical forests are likely to be net sources of carbon rather than sinks. However, our understanding of tropical forest response and feedback to climate change is very limited. Efforts to model climate change impacts on carbon fluxes in tropical forests have not reached a consensus. Here, we use the Ecosystem Demography model (ED2) to predict carbon fluxes of a Puerto Rican tropical forest under realistic climate change scenarios. We parameterized ED2 with species-specific tree physiological data using the Predictive Ecosystem Analyzer workflow and projected the fate of this ecosystem under five future climate scenarios. The model successfully captured interannual variability in the dynamics of this tropical forest. Model predictions closely followed observed values across a wide range of metrics including aboveground biomass, tree diameter growth, tree size class distributions, and leaf area index. Under a future warming and drying climate scenario, the model predicted reductions in carbon storage and tree growth, together with large shifts in forest community composition and structure. Such rapid changes in climate led the forest to transition from a sink to a source of carbon. Growth respiration and root allocation parameters were responsible for the highest fraction of predictive uncertainty in modeled biomass, highlighting the need to target these processes in future data collection. Our study is the first effort to rely on Bayesian model calibration and synthesis to elucidate the key physiological parameters that drive uncertainty in tropical forests responses to climatic change. We propose a new path forward for model-data synthesis that can substantially reduce uncertainty in our ability to model tropical forest responses to future climate.