Industrialization and information and communication technology (ICT) are central to China’s economic transformation, yet their roles in the country

This study aimed to analyze the impact of the digital economy and artificial intelligence (AI) on GDP growth in 10 developed and developing countries during the period 2010–2024. It was based on the hypothesis that increased digitalization and AI investments promote sustainable economic growth by improving national productivity and efficiency, in accordance with modern technological growth theory, which links digital innovation to economic development. The study used tablet data comprising 150 observations, which were analyzed using fixed- and random-effects models, controlling for traditional variables such as employment, human capital, and investment. The results showed that the Digitalization Indicators (DIGI) had a significant positive impact on growth (fixed: 0.003479, p < 0.01; random: 0.003325, p < 0.01), and that investment in AI also had a significant positive impact (fixed: 0.063695, p < 0.05; random: 0.066548, p < 0.05). In contrast, workforce size had a limited impact, while education and human capital emerged as key drivers of sustainable growth (Constant: 0.003257, p < 0.01; Random: 0.003264, p < 0.01). The inclusion of dummy variables further differentiated between developed and developing countries in the random-effects model, reinforcing the economic interpretation of the findings. The study suggests that integrating digitalization, education, and investment in artificial intelligence is an effective strategy for promoting sustainable economic growth, while emphasizing the importance of workforce skills development to maximize its impact.

The BRICS economies, facing the threat of climate change, face a policy challenge in transitioning from fossil-fuel-based energy systems and improving environmental quality. This necessitates urgent policy changes in the outdated energy infrastructure of BRICS countries. However, there still remains a policy gap regarding how countries in the BRICS, a group of rapidly developing economies, can grow their economies in line with the Sustainable Development Goals. The aim of our study is to investigate the impact of green energy, green technology, and forest cover on environmental quality in BRICS countries. The BRICS group of countries offers an ideal field of study for both examining the impacts of green energy, green technology, and forest areas on environmental quality in developing economies, as well as for evaluating national and global energy policies. Although numerous studies have empirically examined the relationship between environmental variables and green initiatives, the impact of green initiatives on the load capacity factor has been overlooked. These studies have generally used various econometric methods and have not included machine learning in the process. This study addresses this research gap by focusing on green energy and green technology, which are preferred for their various advantages and make significant contributions to the load capacity factor. To analyze this relationship in BRICS economies between 2000 and 2022, the Augmented Mean Group (AMG) estimator and Machine Learning algorithms were used. According to the results, strong evidence emerges of a positive relationship between green energy, the digital economy, forested area, and the load capacity factor, while a negative relationship exists between green technology, growth, and the load capacity factor. Based on robust empirical findings, renewable energy sources are a key driver of clean energy adoption and can ultimately increase the load capacity factor in BRICS economies. The results also imply that, since developments in green technological innovation in BRICS countries are still in their infancy, investments in green technologies for a sustainable environment need to be qualitatively increased.

This study provides a quantitative econometric analysis of structural interactions between fossil‐fuel and low‐carbon energy technologies using high‐frequency data for coal, natural gas, crude oil, solar photovoltaics (PVs), onshore wind, hydrogen, and battery storage. The analysis integrates VAR–GFEVD connectedness modeling, dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (DCC–GARCH) dynamic correlation estimation, portfolio optimization, and scenario‐based projections to evaluate volatility transmission, dependency structures, and long‐term system evolution. The results show that fossil‐fuel technologies, particularly coal and natural gas, act as dominant volatility transmitters, while renewable technologies primarily function as net receivers of shocks. Dynamic correlations reveal persistent coupling between natural gas and solar PV, alongside gradual decoupling trends for wind technologies. Portfolio analysis indicates that diversification benefits depend on underlying economic linkages rather than simple technological classification. Regression results further demonstrate that system connectedness is jointly influenced by market volatility, technological cost dynamics, and policy‐related factors. Scenario projections indicate that high‐innovation pathways achieve the most favorable outcomes, system costs declining to ~45 USD/MWh by 2050, emission reductions approaching 80%, and system connectedness decreasing to around 50%–55%. These findings highlight the importance of technological innovation, renewable deployment, and policy stability in reducing systemic risk and enabling structurally resilient energy transitions.