Abstract
The paper's main aim is to forecast the carbon dioxide (CO(2)) emissions in the USA and its related components, analysing the contributions of each of those components to CO(2) total volume. The empirical ground is a mix of non-linear tools, combining the artificial neural network (ANN) parametric method with a vector autoregressive (VAR) estimator. ANN includes 1 layer and 20 neurons, forecasting being based on the economic growth and net trade effects doubled by different types of renewable energy consumption. The accuracy of estimations for 14 targeted categories of CO(2) emissions is ensured by 4360 observations, with 10 types of inputs over 1984M01-2020M04. ANN seems to offer superior forecasting accuracy compared with the widely used autoregressive methods, such as VAR model, but seems to be weak in capturing the output 'spike' forms. The main findings show that, although economic growth and net trade have an important contribution to the targeted outputs, the more prominent ones are wind, solar and total biomass energy consumption. Therefore, the CO(2) emissions can be better controlled through non-polluting capacities, in parallel with the use of wind, solar and total biomass energies. The tool excellently predicts the CO(2) emissions during pandemic crises being a good instrument in policy decisions. Modest contributions to CO(2) prediction seem to have energy consumption generated by waste, hydroelectric power and renewable geothermal systems. This underlines an unclear current status given their collateral effects in environmental damages and high investment costs. The paper contributes to the literature in several ways. It is one of the first works focused on CO(2) emissions forecasting in the USA based on a mixed approach by ANN and VAR types, considering an extended pallet of inputs to predict the volume of total CO(2) emissions but also its components. As a novelty, the inputs combine both economic and environmental determinants. Not at least, the estimations are performed based on a large span, with monthly frequency.