Assessing the relevance of the Granger non-causality test for energy policymaking: theoretical and empirical insights
This paper highlights the relevance of Granger non-causality tests in energy economics research, particularly for informing public policy decisions. While approaches such as CS-ARDL and estimators like AMG and CCEMG are widely used, they do not fully capture the predictive relationships between vari...
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| Language: | English |
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Elsevier
2025-05-01
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| Series: | Energy Strategy Reviews |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211467X25001063 |
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| author | Brahim Bergougui Manuel A. Zambrano-Monserrate |
| author_facet | Brahim Bergougui Manuel A. Zambrano-Monserrate |
| author_sort | Brahim Bergougui |
| collection | DOAJ |
| description | This paper highlights the relevance of Granger non-causality tests in energy economics research, particularly for informing public policy decisions. While approaches such as CS-ARDL and estimators like AMG and CCEMG are widely used, they do not fully capture the predictive relationships between variables. To illustrate this, we revisit the findings of Irfan et al. (2023), who analyzed factors influencing energy transitions in G-7 and E−7 economies using Westerlund's (2007) cointegration method and CS-ARDL. Additionally, we incorporate data from Zhao et al. (2024) to estimate the relationships between artificial intelligence, GDP, trade, population, and energy efficiency using the CS-ARDL approach, complemented by Granger non-causality tests. Our results, in some cases, expand upon the evidence provided by Irfan et al. (2023), while in others, they suggest a different interpretation of key relationships. Specifically, we find that the mineral market does not exhibit significant predictive power over energy transition, whereas trade and economic growth contribute meaningfully to renewable energy development. Furthermore, using data from Zhao et al. (2024), we confirm that incorporating non-causality tests enhances the interpretation of CS-ARDL estimates, demonstrating that these tests provide valuable insights into the directionality of economic and energy relationships, which is important for policy formulation. These findings highlight the importance of integrating non-causality tests with traditional econometric methods to derive more robust and policy-relevant conclusions. |
| format | Article |
| id | doaj-art-ddcab1f41a514c03bdf4a428631c948e |
| institution | OA Journals |
| issn | 2211-467X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Strategy Reviews |
| spelling | doaj-art-ddcab1f41a514c03bdf4a428631c948e2025-08-20T02:31:04ZengElsevierEnergy Strategy Reviews2211-467X2025-05-015910174310.1016/j.esr.2025.101743Assessing the relevance of the Granger non-causality test for energy policymaking: theoretical and empirical insightsBrahim Bergougui0Manuel A. Zambrano-Monserrate1National Higher School of Statistics and Applied Economics (ENSSEA), Koléa, Algeria; International Institute of Social Studies (ISS), Erasmus University Rotterdam, The Hague, the Netherlands; College of Business & Economics, Qatar University, Doha, Qatar; Corresponding author. International Institute of Social Studies (ISS), Erasmus University Rotterdam, The Hague, the Netherlands.Universidad Espíritu Santo, Samborondón, EcuadorThis paper highlights the relevance of Granger non-causality tests in energy economics research, particularly for informing public policy decisions. While approaches such as CS-ARDL and estimators like AMG and CCEMG are widely used, they do not fully capture the predictive relationships between variables. To illustrate this, we revisit the findings of Irfan et al. (2023), who analyzed factors influencing energy transitions in G-7 and E−7 economies using Westerlund's (2007) cointegration method and CS-ARDL. Additionally, we incorporate data from Zhao et al. (2024) to estimate the relationships between artificial intelligence, GDP, trade, population, and energy efficiency using the CS-ARDL approach, complemented by Granger non-causality tests. Our results, in some cases, expand upon the evidence provided by Irfan et al. (2023), while in others, they suggest a different interpretation of key relationships. Specifically, we find that the mineral market does not exhibit significant predictive power over energy transition, whereas trade and economic growth contribute meaningfully to renewable energy development. Furthermore, using data from Zhao et al. (2024), we confirm that incorporating non-causality tests enhances the interpretation of CS-ARDL estimates, demonstrating that these tests provide valuable insights into the directionality of economic and energy relationships, which is important for policy formulation. These findings highlight the importance of integrating non-causality tests with traditional econometric methods to derive more robust and policy-relevant conclusions.http://www.sciencedirect.com/science/article/pii/S2211467X25001063Granger non-causality testsEnergy economicsEnergy transitionEnergy policymakingArtificial intelligence |
| spellingShingle | Brahim Bergougui Manuel A. Zambrano-Monserrate Assessing the relevance of the Granger non-causality test for energy policymaking: theoretical and empirical insights Energy Strategy Reviews Granger non-causality tests Energy economics Energy transition Energy policymaking Artificial intelligence |
| title | Assessing the relevance of the Granger non-causality test for energy policymaking: theoretical and empirical insights |
| title_full | Assessing the relevance of the Granger non-causality test for energy policymaking: theoretical and empirical insights |
| title_fullStr | Assessing the relevance of the Granger non-causality test for energy policymaking: theoretical and empirical insights |
| title_full_unstemmed | Assessing the relevance of the Granger non-causality test for energy policymaking: theoretical and empirical insights |
| title_short | Assessing the relevance of the Granger non-causality test for energy policymaking: theoretical and empirical insights |
| title_sort | assessing the relevance of the granger non causality test for energy policymaking theoretical and empirical insights |
| topic | Granger non-causality tests Energy economics Energy transition Energy policymaking Artificial intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S2211467X25001063 |
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