Energy Transitions over Five Decades: A Statistical Perspective on Global Energy Trends
This study analyzes global energy trends from January 1973 to November 2022, using the “World Energy Statistics” dataset from Kaggle, which includes data on the production, consumption, import, and export of fossil fuels, nuclear energy, and renewable energy. The analysis employs statistical techniq...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/14/5/190 |
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| author | Francina Pali Roschlynn Dsouza Yeeon Ryu Jennifer Oishee Joel Aikkarakudiyil Manali Avinash Gaikwad Payam Norouzzadeh Steven Buckner Bahareh Rahmani |
| author_facet | Francina Pali Roschlynn Dsouza Yeeon Ryu Jennifer Oishee Joel Aikkarakudiyil Manali Avinash Gaikwad Payam Norouzzadeh Steven Buckner Bahareh Rahmani |
| author_sort | Francina Pali |
| collection | DOAJ |
| description | This study analyzes global energy trends from January 1973 to November 2022, using the “World Energy Statistics” dataset from Kaggle, which includes data on the production, consumption, import, and export of fossil fuels, nuclear energy, and renewable energy. The analysis employs statistical techniques such as correlation analysis, quantile–quantile (Q–Q) plots, seasonal decomposition, and seasonal autoregressive integrated moving average (SARIMA) modeling. The results reveal strong positive correlations between nuclear energy production and consumption, as well as between renewable energy production and consumption. Seasonal decomposition highlights annual patterns in renewable energy use and a declining trend in fossil fuel dependency. SARIMA modeling forecasts continued growth in renewable energy consumption and a gradual reduction in fossil fuel reliance. These findings provide critical insights into long-term energy patterns and offer data-driven implications for global energy policy and strategic planning. |
| format | Article |
| id | doaj-art-dc8252ed61ca4b8a8590d202811d6390 |
| institution | OA Journals |
| issn | 2073-431X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj-art-dc8252ed61ca4b8a8590d202811d63902025-08-20T01:56:17ZengMDPI AGComputers2073-431X2025-05-0114519010.3390/computers14050190Energy Transitions over Five Decades: A Statistical Perspective on Global Energy TrendsFrancina Pali0Roschlynn Dsouza1Yeeon Ryu2Jennifer Oishee3Joel Aikkarakudiyil4Manali Avinash Gaikwad5Payam Norouzzadeh6Steven Buckner7Bahareh Rahmani8Department of Computer Science, Saint Louis University, Saint Louis, MO 63103, USADepartment of Computer Science, Saint Louis University, Saint Louis, MO 63103, USADepartment of Computer Science, Saint Louis University, Saint Louis, MO 63103, USADepartment of Computer Science, Saint Louis University, Saint Louis, MO 63103, USADepartment of Computer Science, Saint Louis University, Saint Louis, MO 63103, USADepartment of Computer Science, Saint Louis University, Saint Louis, MO 63103, USADepartment of Professional Studies, Saint Louis University, Saint Louis, MO 63103, USADepartment of Chemistry, Saint Louis University, Saint Louis, MO 63103, USADepartment of Health and Clinical Outcomes Research, Saint Louis University School of Medicine, Saint Louis, MO 63103, USAThis study analyzes global energy trends from January 1973 to November 2022, using the “World Energy Statistics” dataset from Kaggle, which includes data on the production, consumption, import, and export of fossil fuels, nuclear energy, and renewable energy. The analysis employs statistical techniques such as correlation analysis, quantile–quantile (Q–Q) plots, seasonal decomposition, and seasonal autoregressive integrated moving average (SARIMA) modeling. The results reveal strong positive correlations between nuclear energy production and consumption, as well as between renewable energy production and consumption. Seasonal decomposition highlights annual patterns in renewable energy use and a declining trend in fossil fuel dependency. SARIMA modeling forecasts continued growth in renewable energy consumption and a gradual reduction in fossil fuel reliance. These findings provide critical insights into long-term energy patterns and offer data-driven implications for global energy policy and strategic planning.https://www.mdpi.com/2073-431X/14/5/190energy statisticsseasonal decompositionseasonal autoregressive integrated moving average (SARIMA) |
| spellingShingle | Francina Pali Roschlynn Dsouza Yeeon Ryu Jennifer Oishee Joel Aikkarakudiyil Manali Avinash Gaikwad Payam Norouzzadeh Steven Buckner Bahareh Rahmani Energy Transitions over Five Decades: A Statistical Perspective on Global Energy Trends Computers energy statistics seasonal decomposition seasonal autoregressive integrated moving average (SARIMA) |
| title | Energy Transitions over Five Decades: A Statistical Perspective on Global Energy Trends |
| title_full | Energy Transitions over Five Decades: A Statistical Perspective on Global Energy Trends |
| title_fullStr | Energy Transitions over Five Decades: A Statistical Perspective on Global Energy Trends |
| title_full_unstemmed | Energy Transitions over Five Decades: A Statistical Perspective on Global Energy Trends |
| title_short | Energy Transitions over Five Decades: A Statistical Perspective on Global Energy Trends |
| title_sort | energy transitions over five decades a statistical perspective on global energy trends |
| topic | energy statistics seasonal decomposition seasonal autoregressive integrated moving average (SARIMA) |
| url | https://www.mdpi.com/2073-431X/14/5/190 |
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