The current study uses a sample of 283 prefecture-level administrative units, between 2004 and 2015 in China, to study the spatial spillover effects of tourism development on urban economic performance. Positive direct effects of tourism on urban economy are found for the full data set, while the indirect effects are observed dependent on area coverage. Although the analysis of 283 units (full sample) indicates no significant tourism spatial spillover effect, the subsampling statistics reveals the contrary. Substantial spatial competitive inhibition effects emerge in the analysis of within-region subsample, and significantly positive spatial spillover effects are observed in the cross-region subsample.

Human activities have increased greenhouse gas emissions since the Industrial Revolution, and “emission peaking” and “carbon neutrality” have become serious concerns at this point. The role of information infrastructure in reducing carbon emissions is a critical issue that has received little attention and needs to be addressed. Using panel data from 289 cities in China between 2011 and 2017, this research empirically explores the impact of information infrastructure on urban carbon emission intensity and the mechanism behind this effect. We discover that the construction of information infrastructure significantly reduces urban carbon emissions, and this finding holds true after a series of robustness tests. The mechanism is optimization of industrial structure, agglomeration of producer service industries, and innovation of green technologies. According to the heterogeneity test, the carbon emission reduction is greater in mega cities with higher technological levels and larger urban scales, as well as large cities with better traditional infrastructure. The present work’s findings give empirical support for promoting green and low-carbon development and mitigating global warming.

In the context of community-based tourism, this study investigates the determinants of tourist loyalty to homestays, with a focus on sustainable rural tourism development in China. Using the stimulus–organism–response (SOR) framework, we propose a conceptual model to examine how connected experiences influence tourist emotions and, in turn, their loyalty. Data were gathered through onsite surveys of tourists, and structural equation modeling was applied to assess the relationships between key variables. The analysis reveals that tourists’ emotions mediate the effect of connected experiences on loyalty, while the unique local characteristics of homestays significantly moderate the link between emotions and loyalty. These findings provide valuable insights for policymakers and practitioners aiming to enhance the sustainability of rural tourism through strategic management of community-based homestays.

This study empirically evaluates the environmental impact of China’s 2012 Green Credit Guidelines on high-polluting firms, utilizing a panel dataset of A-share listed companies spanning 2006–2022. Applying a Difference-in-Differences (DID) framework, the analysis employs Bloomberg’s ESG environmental score—which comprehensively captures resource efficiency, pollution abatement, and ecological conservation—as a proxy for environmental performance. The findings reveal a statistically significant improvement of 2.3–3.7 points in environmental scores attributable to the policy intervention. According to Bloomberg’s calibration, this improvement corresponds to an estimated annual reduction of 1.2–1.8 million tons of SO₂ emissions, representing 15–22% of China’s mid-term (2020–2025) pollution abatement targets. Robustness checks, including Propensity Score Matching (PSM), placebo tests, and alternative measurements, validate the results. The paper identifies two key mechanisms through which green credit policies affect environmental performance: financing constraints and innovation compensation. Heterogeneity analysis shows that firms in eastern regions, competitive industries, and state-owned enterprises (SOEs) experience greater impacts. This paper contributes to the global discourse on sustainable development, offering evidence of how green credit policies can improve high-polluting firms’ environmental performance based on their region, industry and ownership, and providing recommendations for supporting the transition to a greener economy.

The COVID-19 pandemic imposes new challenges on the capability of governments in intervening with the information dissemination and reducing the risk of infection outbreak. To reveal the complexity behind government intervention decision, we build a bi-layer network diffusion model for the information-disease dynamics that were intervened in and conduct a full space simulation to illustrate the trade-off faced by governments between information disclosing and blocking. The simulation results show that governments prioritize the accuracy of disclosed information over the disclosing speed when there is a high-level medical recognition of the virus and a high public health awareness, while, for the opposite situation, more strict information blocking is preferred. Furthermore, an unaccountable government tends to delay disclosing, a risk-averse government prefers a total blocking, and a low government credibility will discount the effect of information disclosing and aggravate the situation. These findings suggest that information intervention is indispensable for containing the outbreak of infectious disease, but its effectiveness depends on a complicated way on both external social/epidemic factors and the governments’ internal preferences and governance capability, for which more thorough investigations are needed in the future.

Using housing market data of Beijing and Hangzhou, China, we conduct a case study to detect how the difference of urban structure can affect the relationship between the subway system and housing prices. To quantify the characteristics of urban structure, we propose a constrained clustering method, which can not only reveal the spatial heterogeneity of the housing market, but also provides a link between heterogeneity and the underlying urban structure. Applying constrained clustering to Beijing and Hangzhou, we find that the relationship between accessibility to metro stations and housing prices is weak and vulnerable, while the improvement of commuting efficiency, measured by a key variable, the metro index, does have a robust connection to metro premium on housing units. In particular, only a large metro index can be associated with a positive metro premium. Structural features, such as the size of urban core and the existence of multiple sub-centers, influence the metro premium by affecting the value and spatial distribution of the metro index. The evidence from Beijing and Hangzhou supports that in a mono-centric city, the size of the urban core is positively associated with the metro index and the metro premium, while in a poly-centric city with a small urban core, the metro index tends to be lower in the core region and higher in the satellite regions, which enforces the metro premium to be negative in the core while positive outside of the core.