In the context of the digital economy, achieving rural industrial revitalization in China hinges on digitization. This study delves into the synergistic mechanisms of diverse factors that contribute to Rural Industrial Revitalization across three dimensions: technology, organization, and environment. To investigate these mechanisms, a combination of the necessary condition analysis method and the fuzzy set qualitative comparative analysis method is employed. The research findings indicate that no necessary conditions exist for achieving high-level rural industrial revitalization. However, digital infrastructure and the digital financial environment have a universally significant impact. The study identifies four distinct pathways driving high-level rural industrial revitalization: digitaldriven, digital-government-talentdriven, digital-enterprisedriven, and digital-enterprise-talentdriven. Furthermore, significant variations exist in the driving pathways for rural industrial revitalization among the eastern, central, and western regions of China. By unveiling the multifaceted mechanisms underpinning the revitalization of rural industries, this research provides valuable practical insights for the future development of rural industries in China.

This study examined the relationship between environmental regulations (ER) and green economic efficiency (GEE) based on the panel data of 30 provinces in China from 2008 to 2017. Firstly, GEE was calculated and evaluated using the super-efficiency SBM model with undesirable outputs. Secondly, the impact of ER on GEE was studied with the Tobit model. Finally, this article draws conclusions based on the above analysis and offers some suggestions for government and enterprise. The results show that the GEE of China is generally low. The GEE of the eastern region is much higher than that of the middle and western regions, with the western region performing slightly better than the middle. From west to east, there is a V shape, with high efficiency in the west and east and low efficiency in the middle. The impact of ER on GEE has the characteristics of nonlinearity and spatial heterogeneity. At the national level, as well as in the middle and western regions, the impact of ER on GEE shows an inverted U shape that first rises and then falls. ER are currently within the range conducive to the development of GEE. If the intensity of ER exceeds the critical value, they will have a negative impact on GEE. In the eastern region, the impact of ER on GEE is shown as a U shape that first falls and then rises. At present, the ER are not of sufficient intensity to contribute to the improvement of GEE. Only when the intensity of the ER exceeds the critical value will they have a positive influence on the GEE.