Renewable Energy Substitution Model and Environmental Preservation
Excessive usage of fossil fuels, which contain large hydrocarbons, worsens global warming, public health, and ecosystems. The rate of environmental degradation and greenhouse gas emissions is decelerated by switching from fossil fuels into renewables. This research applies time series method to estimate the electric power consumption of 112 countries. Then, a stochastic substitution model is introduced to estimate the contribution of the renewables in the electric power generation. The main part of the model relies on the Bayesian inference and pseudo random number generators (PRNGs) to update the statistical distribution of renewables in the substitution model. The results of the four types of substitution models emphasize on renewables investment as a way to accelerate the substitution and environment preservation.
KeywordsEnergy use forecasting Electric power consumption Bayesian inference Statistical substitution model
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