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.

Amidst the escalating global challenges of deforestation and climate change, effective forest governance has become a critical global imperative. As a key actor in this arena, China presents a crucial case for understanding state-led environmental governance. This study addresses the thematic evolution and governance structure of China’s forest policy planning from 1980 to 2024. Grounded in multi-level governance (MLG) theory, we apply the Non-negative Matrix Factorization (NMF) topic model to a corpus of 1265 policy documents sourced from the PKULaw database, spanning four administrative levels from central to county. An analysis of 13 core policy themes reveals a significant transition, shifting from early regulatory development and resource utilization to a modern emphasis on ecological protection, scientific monitoring, financial support, and governance innovation. The findings delineate a complex governance architecture: a vertical division of labor (central guidance, local implementation), a horizontal model of inter-departmental interaction where specialized management coexists with comprehensive coordination, and adaptive governance reflecting regional heterogeneity. These results illuminate the dynamic evolution of power allocation, central–local relations, and synergy within China’s forest sector. This study not only provides new empirical evidence and an analytical framework for understanding China’s natural resource policy transition but also offers scientific insights for optimizing multi-level forest governance systems and enhancing policy synergy and efficacy.

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.

Despite China’s success in its new energy vehicle (NEV) transition, significant regional imbalances persist, raising the question of why provincial policy effectiveness is so context-dependent. To investigate this, this study develops a novel framework to measure policy “quality” and “style”, systematically quantifying 2455 provincial policy documents from 2013 to 2023. Our empirical analysis reveals that policy quality—encompassing its authoritativeness, instrument strength, and resource commitment—is a far more decisive determinant of effectiveness than sheer policy quantity. We identify three primary policy styles with distinct impacts: substantive-driving policies are crucial for stimulating market demand, whereas coordinative-programmatic policies are more effective in guiding industrial supply, revealing a significant goal-mismatch. Conversely, high-level authoritative policies can unexpectedly inhibit infrastructure development. Crucially, the study finds that provincial policies act more as “catalysts” than “creators”, their effectiveness being highly contingent on local economic, fiscal, and industrial fundamentals. The findings of this research offer direct implications for policymaking: decision-makers should shift their focus from pursuing policy quantity to enhancing policy quality and design targeted, “precision-irrigation” policy instrument portfolios tailored to the specific contexts and development objectives (e.g., promoting sales or guiding production) of different regions.

Connected and Automated Electric Vehicles (CAEVs) are rapidly evolving into complex Cyber-Physical-Social Systems (CPSS), generating structural tensions between technological innovation and public safety. Current research in public governance exhibits significant fragmentation. Scholars frequently isolate data privacy compliance from algorithmic safety auditing, treating them as distinct silos. To bridge this gap, this study applies the PRISMA framework to systematically synthesize 135 core peer-reviewed articles, exposing the endogenous limitations of unidimensional regulatory paradigms. Our analysis yields three central insights. First, traditional “notice-and-consent” models fail under the ubiquitous data collection demands of modern V2X environments. Macro-level policies must translate into foundational Privacy-Enhancing Technologies (PETs) through “Law-as-Code” mechanisms. Second, the opacity of end-to-end algorithmic decision-making deconstructs traditional tort liability systems. This necessitates ex-ante quantitative auditing mechanisms—such as Explainable Artificial Intelligence (XAI) and enhanced Threat Analysis and Risk Assessment (TARA 2.0)—to mitigate adversarial attacks and physical-level safety hazards. Third, overcoming cross-national regulatory fragmentation requires constructing a “dual-track synergistic” governance architecture. This framework institutionalizes the coupling of data lifecycle quality workflows with the algorithmic Safety of the Intended Functionality (SOTIF). Ultimately, this review advocates for adaptive regulatory sandboxes and advances the harmonization and mutual recognition of global standards (e.g., ISO/SAE 21434, UN R155/156). Addressing current methodological and empirical data constraints, future academic inquiry must pivot. Researchers should target the value alignment challenges of Large Language Models (LLMs) in autonomous driving and implement multi-stakeholder participatory policy pilots designed to reconcile diverse social values.