Heterogeneous panel data model with sharp and smooth changes : testing green growth hypothesis in G7 countries

This study examines the complex relationship between renewable energy consumption and economic growth in G7 countries, emphasizing the critical role of renewable energy in addressing climate change and facilitating the transition to a low-carbon economy. Using heterogeneous panel data models, the study incorporates both sharp and smooth structural changes through the Fourier Seemingly Unrelated Regressions Mean Group (F-SURMG) estimation. This approach effectively addresses heterogeneity, structural changes, and cross-sectional dependency in panel data analysis, ensuring robust and reliable insights. The findings reveal significant variation in the impact of renewable energy consumption on economic growth across the G7 countries. In Japan, the effect is positive and statistically significant, supporting the green growth hypothesis. This outcome is attributed to Japan's strong tradition of technological innovation, which enables effective integration and adaptation of renewable energy technologies. Conversely, Italy exhibits a negative and significant impact, highlighting challenges in aligning renewable energy with its economic framework. For Canada, France, Germany, the UK, and the US, the effect is not statistically significant, suggesting that renewable energy consumption has not yet become a major driver of economic growth in these nations. The study underscores that the transition to green growth in G7 countries faces several obstacles, including outdated infrastructure, volatile energy prices, competition from fossil fuels, and insufficient investment in renewable technologies. These factors can impede innovation and limit the economic contributions of renewable energy. Addressing these challenges is crucial for unlocking the full potential of renewable energy to foster sustainable economic growth across the G7 countries.