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Causal Dynamic Relationships between Political–Economic Factors and Export Performance in the Renewable Energy Technologies Market

doi: 10.3390/en11040874
This study explores how political–economic forces could affect export performance in the renewable energy technologies market. We conduct panel framework analyses to verify the characteristics of panel data for 19 countries before establishing the panel estimator meant to test the effects of political–economic forces on export specialization. We consider the results of the panel framework analyses and develop an empirical model to test casual dynamic relationships between political–economic forces and export performance. The results from the least squares dummy variable-corrected estimation indicate that the major factors promoting the export specialization of renewable energy technologies are, in order of decreasing importance, public pressure, market size, and government demand-pull policy. However, the traditional energy industry has no significant effect on export performance. Finally, this study finds that dynamic effects exist in all estimations.
- Kyonggi University Korea (Republic of)
- Yanbian University China (People's Republic of)
- Kyonggi University Korea (Republic of)
- Yanbian University China (People's Republic of)
Technology, political–economic forces, T, political–economic forces; export performance; panel vector autoregressive model, export performance, panel vector autoregressive model
Technology, political–economic forces, T, political–economic forces; export performance; panel vector autoregressive model, export performance, panel vector autoregressive model
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).10 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
