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Despite having multidimensional positive externalities of renewable energy (RE) in theory, the literature on RE's economic and environmental determinants is still very nascent. We have addressed this research gap by considering a balanced panel of 38 OECD nations attempting a phase-by-phase energy transition. We have used data spanning from 2000 to 2020 and deployed the novel method of moments quantile regression (MMQR) approach. Our results unveil that economic complexity reduces the consumption of renewable energy among the OECD nations, indicating that as the export becomes diversified, energy consumption at the production level becomes more dependent on non-renewable sources. This further suggests that OECD nations are yet to meet their renewable energy generation threshold. Moreover, energy efficiency fosters the consumption of renewable energy, substantiating that energy efficiency and renewable energy share complementary co-movement among the OECD nations. Furthermore, we unravel that environmental technology and natural resource rent have heterogeneous effects on renewable energy consumption in OECD. For instance, environmental technology only boosts RE consumption at the higher quantiles (70th to 95th), whereas revenue from natural resource rent curbs RE consumption. Our conclusions hold after a series of robustness checks. Overall, we propose that OECD nations should only export those products with absolute comparative advantage to reduce excessive dependence on fossils. Additionally, investment in renewable energy technology development should be escalated to ensure energy efficiency in the long-term. • The impacts of economic complexity (ECI), environmental technology (ENT), natural resource rent (NRT) and energy efficiency (EFX) on green energy use explored. • We use novel non-parametric method of moments quantile regression approach for OECD countries. • The ECI curbs the consumption of renewable energy while EFX is a boon for it. • ENT and NRT have heterogenous effects on RE consumption. • We also note different causal relationships to rectify the results' robustness. [ABSTRACT FROM AUTHOR]

Transitioning to sustainable energy is a global imperative for countries to develop policies to achieve Sustainable Development Goals (SDGs), particularly SDG 7 and 13. Given the possible association between digital inclusion (DIGI) and the energy transition (EGTN), we examined the impact of DIGI on the EGTN while controlling for environmental taxes (ENTX) in the top-5 greenest economies in the world using quarterly data from 2001 to 2021. We examined the association at short-, medium-, and long-term frequencies using comprehensive approaches such as the Method of Moments Quantile Regression (MMQR) for panel analysis and Wavelet techniques, i.e., wavelet coherence, multiple wavelet, and partial wavelet for country-specific assessment. According to the MMQR results, DIGI has a significant and positive effect on EGTN, with a coefficient value ranging from 10.95 to 5.980 while moving from lower (0.25) to higher (0.95) quantiles. Similarly, ENTX has a positive and significant effect on EGTN, with a coefficient value ranging from 1.770 to 3.132 as the quantiles increase. In addition, the results of wavelet coherence expose a strong and positive connectedness between DIGI and EGTN in each sample country. Furthermore, multiple wavelet results for each country demonstrated that ENTX significantly strengthens the interaction between DIGI and EGTN. Our findings result in developing SDGs-oriented policy guidelines, specifically the policy strategy for addressing SDG-7 and SDG-13. The study endorses that the sample countries take initiatives to accelerate the EGTN process by improving digital inclusion and implementing an effective environmental taxation system. • We evaluated the role of digital inclusion on energy transition. • The study also considers the role of environmental taxes. • The study employed panel data from 2001 to 2020 for the greenest economies. • Digital inclusion and environmental taxes promote energy transition. • The panel results are validated by the wavelet analysis. [ABSTRACT FROM AUTHOR]