Jinyang Cai

Digital economy development is important for promoting industrial upgrades and high-quality economic growth. As excessive carbon emissions are a threat to sustainable economic growth, whether digital economy development contributes to reducing carbon emission intensity has gained attention. This study employs the fixed effects model and spatial Dubin model to examine the effect of digital economy development in 275 cities in China on the reduction of carbon emissions intensity. The results show effectiveness in reducing carbon intensity with a spatial spillover effect to neighboring areas. Urban economic primacy plays a positive modulating role in carbon reduction. Policy implications are discussed.

This research utilizes a bootstrap rolling-window (BRW) causality test to explore the causal interrelationship between nuclear energy consumption (NUC) and carbon dioxide emissions (CO₂) in 6 developed countries from 1980 to 2020. When there are structural shifts in the full-sample time series, empirical research exploring causality between two-time series generates erroneous conclusions. On the other hand, the BRW method allows researchers to find potential time-varying causality between time series using sub-sample data. The outcomes of the BRW causality test disclosed the following results: (i) a unidirectional negative causality from NUC to CO₂ without feedback was found for Japan; (ii) a negative causality at sup-sample periods from NUC to CO₂ surfaced at the sub-sample period while a positive causality surfaced from NUC to CO₂ in sub-sample period for the United States of America (USA) and France; (iii) a negative feedback causality between NUC and CO₂ was found For Canada; (iv) a positive unidirectional causality surfaced from NUC to CO₂ was found for Germany, which implies that consumption of NUC worsens the environment in the sub-sampled period. The results may have policy consequences for the selected developed countries regarding NUC and CO₂ nexus.

Since a variety of barriers pose challenges to the Indian wind energy sector, the extent to which these barriers hamper this sector and the alternative solutions are largely unknown. We identify several barriers using existing literature, and then using the modified Delphi approach, refine 25 barriers and classify them into five significant dimensions. Later, the Analytical Hierarchical Process determined the ranking of barriers using pairwise comparison matrices. The Grey Technique for Order Preference by Similarity to Ideal Solution method ranked alternative solutions to these barriers. Results indicate that “financial barrier” is the most important barrier among all dimensions, while “limited government subsidy” is most influential among all sub-barriers. “Availability of adequate funds” is the best alternative to overcome these barriers. Finally, a sensitivity analysis is performed to validate the study findings. The study findings may assist practitioners and policymakers in boosting the current sluggish growth of the Indian wind sector.