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<p>In the current context, the focus on optimizing energy distribution is intensified by high-impact events such as resource scarcity, import dependence, war, inflation, or environmental threats. As a consequence, international agencies have built extensive policies and targets that will reflect directly into the general energy distribution. One of the most crucial would be the 65% share of electricity that is bound to be generated from renewables until 2030. To study the feasibility of that goal, we will analyze the evolution of the energy mix worldwide in the last two decades, identifying the shift towards alternative energy sources, while also pinpointing the agents of change in terms of the low-carbon transition. The methodology will consist of a bootstrap clustering algorithm that has been computed for 3 moments: 2000, 2010, and 2020, generating clear differences in terms of energy distribution. In the second part of the paper, we have employed an ARIMA model that aims to predict the share of renewable energy by 2030, with the worrying conclusion that if the current rhythm continues, the goal will not be met and the climate could face severe consequences. Therefore, the paper significantly contributes to the methodological void in terms of bootstrap clustering applicability, while also illustrating a complete energy picture from a geographical and time perspective.</p>
</abstract>

In the current context, the focus on optimizing energy distribution is intensified by high-impact events such as resource scarcity, import dependence, war, inflation, or environmental threats. As a consequence, international agencies have built extensive policies and targets that will reflect directly into the general energy distribution. One of the most crucial would be the 65% share of electricity that is bound to be generated from renewables until 2030. To study the feasibility of that goal, we will analyze the evolution of the energy mix worldwide in the last two decades, identifying the shift towards alternative energy sources, while also pinpointing the agents of change in terms of the low-carbon transition. The methodology will consist of a bootstrap clustering algorithm that has been computed for 3 moments: 2000, 2010, and 2020, generating clear differences in terms of energy distribution. In the second part of the paper, we have employed an ARIMA model that aims to predict the share of renewable energy by 2030, with the worrying conclusion that if the current rhythm continues, the goal will not be met and the climate could face severe consequences. Therefore, the paper significantly contributes to the methodological void in terms of bootstrap clustering applicability, while also illustrating a complete energy picture from a geographical and time perspective.

Assessing the Evolution of the Energy Mix Worldwide, with a Focus on the Renewable Energy Transition

Bucharest University of Economic Studies, Bucharest, Romania

Management & Marketing

This work is licensed under the Creative Commons Attribution 4.0 International License.

In the current context, the focus on optimizing energy distribution is intensified by high-impact events such as resource scarcity, import dependence, war,...

Assessing the Evolution of the Energy Mix Worldwide, with a Focus on the Renewable Energy Transition

Published Online: Dec 18, 2023

Page range: 384 - 397

DOI: https://doi.org/10.2478/mmcks-2023-0020

Keywords
energy mix, renewable energy, bootstrap clustering, time-series, prediction

© 2023 Stelian Stancu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Assessing the Evolution of the Energy Mix Worldwide, with a Focus on the Renewable Energy Transition

Published Online: Dec 18, 2023

Page range: 384 - 397

DOI: https://doi.org/10.2478/mmcks-2023-0020

Keywordsenergy mix, renewable energy, bootstrap clustering, time-series, prediction

© 2023 Stelian Stancu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
energy mix, renewable energy, bootstrap clustering, time-series, prediction