Since 2015, China has witnessed a rapid increase in new energy vehicle (NEV) market penetration, achieving global leadership in this sector. This study employs social network analysis (SNA) and Granger causality tests to examine how policy coordination has influenced China’s NEV market development from 2015 to 2023. We evaluated policy coordination using six network metrics: network density, average path length, transitivity, average clustering coefficient, number of components, and size of largest component. Our findings reveal both correlative and causal relationships between policy coordination and market performance. The analysis demonstrated strong positive correlations between network metrics and market performance indicators (ρ = 0.800–0.850, p < 0.01), while Granger causality tests identified significant temporal effects, particularly in the long term (F = 284.051–281,486.748, p < 0.001). Notably, the largest component size shows immediate causal effects on market performance (F = 4.152, p < 0.05). Based on these results, we recommend establishing a multi-level policy coordination system, optimizing the policy network structure with emphasis on core components, implementing dynamic policy adjustment mechanisms considering time-lagged effects, and strengthening collaborative supervision of policy implementation to further advance China’s NEV market development.

Introduction: The ready-to-cook (RTC) foods sector represents a rapidly growing industry where innovation patterns and technological evolution are pivotal for sustainable development. This study establishes a comprehensive “space-time-technology-network” analytical framework to systematically investigate China’s RTC patent innovation ecosystem. Methods: We analyzed 1,180 patent records through an integrated approach combining time-series analysis, spatial distribution mapping, technology classification, and social network analysis to reveal multidimensional characteristics of RTC innovation. Results: Our analysis identified three distinct evolutionary phases in RTC patents: an embryonic stage (2018–2021), a boom period (2022–2023), and a current adjustment phase (2024). Innovation activities demonstrated marked regional concentration, forming three primary clusters: the Pearl River Delta, Yangtze River Delta-Shandong, and Fujian-Hunan regions. Technologically, the sector is dominated by food processing & forming technology and packaging technology, which have evolved into a highly interconnected “small world” network structure. While enterprises constitute the primary innovation drivers (76.54%), the collaboration network remains notably fragmented. Knowledge flows occur primarily through three channels: intra-corporate group collaborations, industry-university-research partnerships, and regional industrial clusters. Discussion: The RTC patent ecosystem exhibits four defining characteristics: explosive growth, regional agglomeration, multi-technology convergence, and fragmented collaboration networks. This study provides both a theoretical framework and empirical basis for understanding innovation dynamics of emerging food industries and offers valuable insights for promoting high-quality development in the RTC foods sector.

Rural tourism is a key engine for sustainable development, elevated to a strategic level under China’s “Rural Revitalization Strategy”, creating a complex multi-level governance (MLG) policy system whose internal mechanisms are not fully understood. This study aims to analyze the thematic structure, spatio-temporal evolution, and transmission mechanisms of China’s rural tourism policy across central, provincial, and city/county levels. We applied BERTopic topic modeling and spatio-temporal analysis to a corpus of 1174 policy documents from 2005 to 2024. The results reveal a “centrally guided Type I governance” model with a clear functional division: the central level acts as a “top-level strategic designer”, the provincial level as a “key regional hub” for adaptation, and the city/county level as the “frontline of policy implementation”. We identified a vertical transmission chain characterized by a 1–2-year lag, alongside spatial differentiation driven by regional resource endowments at the provincial level and functional needs at the city/county level. This study concludes that China’s rural tourism governance framework is an efficient synergistic system that combines strong central guidance with dynamic local adaptation, providing empirical support for MLG theory in a unitary state and offering insights for optimizing policy coordination.

Reconciling strategic alignment with local innovation is a core governance challenge for policy experimentation zones worldwide. This study investigates this challenge by elucidating the operational dynamics of multi-level governance in China, using the unique Guangdong-Macao In-depth Cooperation Zone in Hengqin as a natural laboratory for institutional experimentation. Integrating semantic network analysis (SNA) and Latent Dirichlet Allocation (LDA) topic modeling, we systematically analyze 623 policy documents from 2009 to 2024, spanning four administrative levels: national, provincial, municipal, and cooperation zone. Our findings reveal a “functional differentiation pyramid” governance architecture, where the national level provides strategic guidance, while lower-level governments engage in significant issue disaggregation and reorganization during implementation. We find that policy transmission across levels is not a linear replication but a dynamic process we term “adaptive reconstruction”—a form of institutional work actively undertaken by subnational governments to enhance policy fitness. We argue that this mechanism is key to how the governance system simultaneously maintains strategic coherence and local flexibility. The results provide new empirical evidence for multi-level governance (MLG) theory. This “Hengqin model” contributes to global governance debates by empirically demonstrating a distinct architecture for policy implementation, while also showcasing the potential of computational social science in policy analysis.

This study investigates the deep-seated impacts of the COVID-19 pandemic on Macao, a mono-economy extremely dependent on the single factor of “tourism mobility”. We investigate a counter-intuitive phenomenon observed during the 2020–2022 shock: the Herfindahl–Hirschman Index (HHI) declined significantly, suggesting “apparent diversification”. Using counterfactual simulations and a Two-Way Fixed Effects (TWFE) model, we quantitatively deconstruct this “resilience illusion”. The results confirm that the decline in the HHI was driven entirely by the “denominator effect” triggered by the collapse of the dominant industry’s (gaming) GVA; if the impact of this recession is excluded, the Counterfactual HHI was even higher than pre-pandemic levels, indicating that the structure did not undergo substantive optimization. Furthermore, inferential statistical tests confirmed the existence of significant “structural lag” in the labor market. This study further reveals a dual divergence mechanism based on “skill specificity”: in sectors with high skill universality (e.g., transport and catering), a structural shift toward “workforce casualization” occurred, manifested by a significant decline in the full-time ratio; conversely, in sectors with strong skill specificity (e.g., gaming and hospitality), firms tended toward “labor hoarding”. This study exposes the macro-indicator trap faced by tourism mono-economies under extreme shocks and provides new micro-evidence for understanding the heterogeneous scars in the service labor market.