The deep integration of the digital and real economies is a critical force reshaping the global economic landscape. At the same time, new high-quality productive forces that are distinguished by their high level of efficiency, quality, and technology mark a new phase in productivity evolution. Understanding the spatial interplay between these two phenomena is crucial for coordinated development. This study empirically investigates their bidirectional relationship and spatial spillover effects. With panel data from 30 provinces in China (2011–2022), we use the Generalized Spatial Three-Stage Least Squares (GS3SLS) approach to estimate a spatial simultaneous equations model. The results reveal a significant bidirectional positive correlation, with the promotional effect of digital–real integration on new quality productive forces being slightly stronger. However, we also identify significantly negative spatial spillover effects between them. These findings underscore the necessity of strengthening their interactive development, fostering interregional cooperation, and optimizing source allocation to alleviate adverse spillovers. This study systematically examines their spatial dynamics and proposes policy recommendations to foster the coordinated advancement of both digital–real integration and New Quality Productive Forces.

Based on panel data from 274 prefecture-level cities in China (2011–2022), this study employs a peer effects model to examine three questions: whether peer effects exist in sustainable rural development, what mechanisms underlie them, and which regions are imitated. It thereby offers a new perspective on the endogenous drivers of rural development. The main findings are as follows. Baseline regression results confirm a significant positive peer effect on rural sustainable development. This result remains robust after a series of tests addressing endogeneity and robustness, including the replacement of explanatory variables, data indentation, exclusion of provincial capitals, placebo tests, and instrumental variable estimation. Heterogeneity analysis reveals that central and western regions are more inclined to learn from other cities in the process of sustainable rural development, whereas the eastern region leans more toward innovation. After the Rural Revitalization Strategy was introduced in 2017, regions have actively explored new rural development models, leading to a decline in the peer effects coefficient compared to the pre-2017 period. Mechanism analysis indicates that both learning-based imitation and competitive imitation serve as channels for peer effects in rural sustainable development. A region’s own development experience does not suppress peer effects. Economically more developed regions are more likely to become imitation targets. Moreover, performance pressure on local officials and the degree of competition among prefecture-level cities strengthen the peer effects. After reclassifying peer groups based on economic structure and geographical location, results show that in the process of rural sustainable development, local governments primarily learn from other regions within the same province that share similar economic structures and are geographically proximate. Based on these findings, this paper proposes differentiated policy recommendations to support sustainable rural development.

In the context of global digital transformation, scientifically examining the spatio-temporal evolution patterns and transition mechanisms of the digital economy at the municipal level is crucial for promoting coordinated regional development. This study takes 281 prefecture-level cities in China from 2011 to 2023 as its research units. Exploratory Spatio-Temporal Data Analysis (ESTDA) is employed to analyze its spatio-temporal dynamics, while a panel quantile regression model nested with spatio-temporal transition types is used to reveal the driving mechanisms. The findings indicate that (1) the overall development level of China’s municipal digital economy has steadily increased, yet significant regional heterogeneity persists, characterized by a pattern of “eastern leading, central fastest-growing, and western lagging,” with considerable room for overall improvement. (2) The digital economy exhibits a significant positive spatial correlation. High–high agglomeration areas remain stable in the southeastern coast, whereas low–low agglomeration areas are concentrated in the central-western and northeastern regions. The spatial pattern demonstrates strong stability and path dependence. (3) LISA time paths reveal drastic changes in local spatial structures in provinces such as Heilongjiang, Inner Mongolia, Hubei, Guangdong, and Guangxi, while East and Central China remain relatively stable. Tortuosity analysis indicates that spatial linkages in the western region are becoming active yet unstable. (4) The quantile regression nested with transition types identifies four mechanisms: “Economic development-Technological innovation” serves as the fundamental driving mechanism across all regions. Low-quantile areas face a complex situation with dual suppression from “opening-up and urbanization” coexisting with drivers from “human capital, government intervention, and industrial structure.” High-quantile areas are synergistically driven by “urbanization, human capital, government intervention, and advanced industrial structure.” This study provides a decision-making reference for overcoming the dilemma of “low-level club convergence” in digital economy development and formulating differentiated regional policies.

This study establishes a municipal-level evaluation system for rural revitalization in China, grounded in the five-sphere integrated framework encompassing “prosperous industries, livable ecology, civilized rural customs, effective governance, and affluent life.” Employing methodologies including the entropy weight-coupling coordination model, LISA spatiotemporal analysis, and multi-scale geographically weighted regression (MGWR), it empirically investigates the evolution and driving mechanisms of rural revitalization development across 282 prefecture-level cities from 2011 to 2023. The findings reveal: (1) Nationwide and regional rural revitalization levels demonstrate a consistent upward trajectory, progressing from a state of “Mild Disorder” to being “On the Verge of Disorder,” with a distinct gradient pattern of “Eastern Region > National Average > Central Region > Western Region.” (2) Significant global spatial correlation is observed, manifesting as polarization typified by “high–high” and “low–low” agglomeration, alongside notable volatility in Northeast and Southwest China. (3) Influencing factors display marked spatiotemporal heterogeneity. Agricultural production efficiency (North China) and technological innovation (nationwide, except the Yangtze River Delta) significantly foster rural revitalization. Conversely, economic development level (Northeast, Central, and Western China), government intervention (Northeast China), and industrial structure upgrading (Northwest China) exhibit constraining effects. The localized positive impacts of urbanization (border areas of Yunnan, Heilongjiang, Sichuan, Jilin, and Tibet) and opening up (border ports) are increasingly evident. Building on these insights, the study proposes recommendations—such as implementing differentiated regional policies, innovating spatial governance models, and activating multidimensional drivers—to overcome the “low-level lock-in” predicament and advance comprehensive rural revitalization. Furthermore, this paper reveals the patterns of multidimensional system coupling and the spatial heterogeneity of driving mechanisms. These findings provide a reference for deepening the understanding of geographical complexity within global sustainable development theory.