Time Series Vector Autoregression Prediction of the Ecological Footprint based on Energy Parameters
This work addresses sustainability and climate change monitoring by providing predictions for policymakers, but it is incremental as it applies an existing method to new data in this domain.
The paper tackled forecasting the ecological footprint using energy parameters by developing a time series vector autoregression model, predicting that the ecological footprint and primary energy consumption will increase while coal energy consumption declines from 2015 to 2024.
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.