Time Series Vector Autoregression Prediction of the Ecological Footprint based on Energy Parameters
Abstract
Sustainability became the most important component of world development, as countries worldwide fight the battle against the climate change. To understand the effects of climate change, the ecological footprint, along with the biocapacity should be observed. The big part of the ecological footprint, the carbon footprint, is most directly associated with the energy, and specifically fuel sources. This paper develops a time series vector autoregression prediction model of the ecological footprint based on energy parameters. The objective of the paper is to forecast the EF based solely on energy parameters and determine the relationship between the energy and the EF. The dataset included global yearly observations of the variables for the period 1971-2014. Predictions were generated for every variable that was used in the model for the period 2015-2024. The results indicate that the ecological footprint of consumption will continue increasing, as well as the primary energy consumption from different sources. However, the energy consumption from coal sources is predicted to have a declining trend.
Keywords
Cite
@article{arxiv.1910.11800,
title = {Time Series Vector Autoregression Prediction of the Ecological Footprint based on Energy Parameters},
author = {Radmila Janković and Ivan Mihajlović and Alessia Amelio},
journal= {arXiv preprint arXiv:1910.11800},
year = {2019}
}
Comments
8 pages, 3 figures, accepted at 5th Jubilee Virtual International Conference on Science, Technology and Management in Energy (eNergetics 2019)