With economic growth and rising incomes, increasing consumption of fossil energy is leading to environmental pollution and climate change, which requires increased innovative inputs to promote the efficiency of renewable energy use. Considering the important impact of innovation input and climate change on renewable energy consumption, greenhouse gas emissions, and green economic growth, this study uses simultaneous equation and sys-GMM model to explore the dynamic nexus of innovation input, climate change, and energy-environment-growth in OECD and non-OECD countries, with panel data covering 2000 to 2019. The empirical results show that renewable energy consumption in non-OECD countries significantly promoted green economic growth, while OECD countries did the opposite. Moreover, renewable energy consumption significantly reduces greenhouse gas emissions caused by climate change, especially for OECD countries. When the level of economic growth exceeds a certain inflection point, greenhouse gas emissions begin to turn from positive to negative, which further verifies the EKC hypothesis. In addition, this study found that innovation input has significantly increased renewable energy consumption, reduced greenhouse gas emissions, and promoted green economic growth in OECD countries. Finally, this study also found that the impact of innovation input in OECD and non-OECD countries on the energy-environment-growth nexus is greater in the short term and more significant in the medium and long term, while the impact of climate change on the energy-environment nexus in OECD and non-OECD countries is more significant in the medium and long term.

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.

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.

Digital technologies help increase citizen engagement and engagement. How does it help and promote civic engagement? What is the difference between E-participation and offline participation? The article selects three public consultation activities on real estate (cultural relics) evaluation carried out by the Macao government from 2015 to 2021 as cases. Research shows that, after years of development, electronic participation is still not the main way for citizens to participate, but the expression of opinions and emotional distribution of online participation are more diverse than offline participation. The study found that compared with traditional models such as offline participation, E-participation is more open, inclusive and convenient, providing a more open channel for the public to participate in public consultation activities, providing a relaxed expression environment and extensive communication opportunities, and enhancing the public’s willingness to express their true feelings. Second, through the open, convenient communication and influence of electronic participation, the influence and effectiveness of public opinion have also been significantly improved. Compared with the "black box" of opinions that may appear in traditional methods, online opinions are more open and transparent, allowing different opinion holders to speak more willingly and dare to speak. Therefore, it is suggested to strengthen electronic participation in public consultation. On the one hand, it should meet the participation requirements of the information technology era and enrich the channels for the public to participate in public affairs in the future; on the other hand, it can promote the enthusiasm of public participation and make public opinions convenient, diverse and true.

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.