Radmila Jankovic, Ivan Mihajlovic
Sustainability became the most important component of world development, as countries worldwide fight the battle against 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.
@article{3ef4e459-13c7-4d5a-83a4-e95ee1fc1397,
title={Time Series Vector Autoregression Predic},
author={Radmila Jankovic and Ivan Mihajlovic},
year={2026},
language={en}
}TY - JOUR TI - Time Series Vector Autoregression Predic AU - Radmila Jankovic AU - Ivan Mihajlovic PY - 2026 LA - en ER -
Mudila Dhanunjaya Rao, Kamalesh K. Singh
Rapid global technological development has resulted in increased production of electronic waste, which presents both challenges and opportunities in r
Jennifer Namias, Dr. Nickolas J. Themelis
This study explores the future of electronic waste recycling in the United States, addressing the challenges and proposing domestic solutions. The rap
P Card, J Canterford
This document presents a collection of papers from the METPLANT 2011 conference focusing on metallurgical plant design and operating strategies. The o
This paper addresses the challenge of assessing the feasibility of wind power plant projects at sites with insufficient or no local historic wind data
Important advances in electrochemical engineering technology over the last three decades have fostered the development of a lternative methods to alle