This study analyses the drivers that impact innovation on offshore wind energy for a select group of countries, applying the quantile and GMM approaches for a period between 2010-2019. The OLS results from the quantile analysis say the log of trademark, Carbon emissions, offshore wind capacity, and electricity from renewable energy are significant and impact on innovation regarding offshore wind energy. Generally, the Breusch-Pagan / Cook-Weisberg test for heteroskedasticity test reveals the variables have a constant variance, confirming the robustness of the findings. The quantile regression depicts that at 25th and 75th quantiles levels, the log of trademark, the log of trade flows, the log of scientific and technical journals quantile coefficients is significantly different from zero and exhibit varied effects on the explained variable patent.Similarly, the analysis applied the IV-GMM estimation in ivreg2 to identify the over restrictions, the Hansen J statistic, and give the robust moment of conditions analysis. The findings are consistent with prior analysis with the log of trademark, the log of offshore wind capacity, the log of carbon emissions, Scientific and technology journals, the log of patent, electricity from renewables to be significant and impact on innovation.The robustness was done on the GMM models, by applying the Huber-White-Sandwich estimator of the variance of the GMM linear models approximators. The ivreg2 robust analysis revealed that the estimates are efficient for homoskedasticity and Statistics robust to heteroskedasticity.Ultimately, the interaction term ‘’cross’’ came out significant in the analysis. Signifying the importance of the interaction variables in scaling innovation.This study will sever as a reference document for policy formulators regarding scaling up innovation for offshore wind energy.