Yingyue Sun

The layout of urban blue, green and grey spaces plays a crucial role in flood risk, but the potential trade-offs and synergistic effects of these spaces on urban flood risk have not been fully studied. This research uses the spatial Durbin model to analyze the direct impact of changes in urban blue, green and grey spaces on risk changes and the spatial spillover effects, revealing how the balance and coordination states and their changes affect urban flood risk. The results show the following: (1) An increase in the balance effect is often accompanied by an increase in risk, but there are differences in the intensity and direction of response at different stages, and the risk changes show significant spatial dependence and spillover effects. (3) The analysis of the spatial Durbin model indicates that the increase in blue and green spaces mainly alleviates risks through local retention and storage. It is notable that in the later stage, the neighborhood spillover effect of green spaces has exceeded its direct local effect, highlighting the collaborative value of green network connectivity at the regional level; while the impact of grey spaces is the most complex—although the construction of sponge cities and urban rainwater management have led to a negative direct local effect (2020–2024), its positive neighborhood spillover effect still persists. Clearly understanding how “balance and coordination” affects risk at different development stages can help urban managers formulate differentiated strategies for the proportion of blue, green and grey spaces based on the development stage of the city, thereby avoiding the use of “one-size-fits-all” planning standards.

Ice–snow tourism in Northeast China is developing rapidly. Against this backdrop, revealing the spatial network structure of ice–snow tourism cities and assessing their disturbance resistance capacity is of great significance for achieving high-quality development of regional ice–snow tourism. This study takes 25 cities across the three northeastern provinces as network nodes, using data covering the period from January 2024 to March 2025. Integrating a complex network analysis framework, this paper comprehensively employs an accessibility model, tourism symbiotic linkage intensity model, and core–periphery model to distinguish core and peripheral cities within the network, analyze its structural characteristics and spatial patterns, and evaluate network vulnerability by simulating two scenarios: random attacks and deliberate attacks. The results indicate that: (1) Accessibility presents a concentric zonal pattern that attenuates gradually from the center to the periphery, accompanied by pronounced north–south disparities. Urban symbiosis intensity is strongly influenced by transportation distance, exhibiting a distinct proximity symbiosis pattern. (2) An ice–snow tourism symbiotic network has initially taken shape among northeastern cities. The network displays small-world properties; however, urban development is unbalanced, with marked hierarchical differentiation. Based on geographic location and resource endowments, the network can be divided into four cohesive subgroups. (3) The symbiotic network proves robust under random attacks, whereas connectivity declines sharply under deliberate attacks, embodying typical “robust-yet-vulnerable” structural characteristics. Both expanding the scale of core nodes and optimizing inter-node connection weights can significantly enhance network robustness. The static identification and dynamic dependency evaluation framework constructed in this study can effectively identify key nodes and vulnerable links within ice–snow city networks, and can serve as a reference for the coordinated development and structural optimization of ice-snow tourism in Northeast China.

Rural tourism is one of the important measures used to realize the rural revitalization strategy in China. At the same time, the “action of prospering the border and enriching the people” is a crucial link in driving the economic development of border areas. With the continuous improvement of the G331 section of the Jilin Line, the development of rural tourism along the border has been accelerated. Therefore, reducing the livelihood vulnerability of farmers is conducive to promoting the rural revitalization strategy in China and consolidating the achievements of poverty alleviation and difficulties in tackling them. This paper takes Tumen City, Yanbian Korean Autonomous Prefecture, Jilin Province, as the sample area; takes Mapai Village, Bailong Village, Hexi Village, Liangshui Village, and Tingyan Village as the research objects; obtains survey data for 224 households through on-the-spot investigation; constructs an evaluation index system for farmers’ livelihood vulnerability in border rural tourist destinations based on the sustainable livelihood theory and the analysis framework of “exposure–sensitivity–adaptability”; calculates farmers’ livelihood vulnerability in this research area by the entropy method and the comprehensive index method; and classifies different villages’ and farmers’ livelihood vulnerability by the natural breakpoint method. By using the multiple linear regression method, this paper analyzes the factors influencing farmers’ livelihood vulnerability in border rural tourist destinations. The results show the following: (1) The overall livelihood vulnerability of farmers is negative, indicating that farmers have a certain ability to resist external risks, but the livelihood vulnerability of some investigated villages is positive, indicating that farmers’ ability to resist external risks is weak, which is closely related to village geographical environment, tourism market environment, family characteristics, and other factors. (2) The livelihood vulnerability of villages with relatively perfect tourism formats is low, which shows that tourism can effectively reduce the livelihood vulnerability of farmers to a certain extent. (3) The household head’s education level, the total price of agricultural machinery, annual income, the ability to borrow money, family size, the number of disabled people, and other factors have a significant influence on the livelihood vulnerability of farmers.

The sustainable development of urban agglomerations depends on the effective coordination of water, energy, food, and ecology (WEFE) systems. However, disparities in resource endowments and socio-economic conditions create challenges for achieving a balanced WEFE system across urban regions. This study examines three urban agglomerations in China with distinct development gradients: the Pearl River Delta (PRD), the Hohhot–Baotou–Ordos–Yulin (HBOY) region, and the Central Jilin Province (CJP). A comprehensive evaluation index system is constructed to assess the coupling coordination degree (CCD) of the WEFE system from 2008 to 2022. Through the CCD model, spatiotemporal evolution trends are analyzed, while correlation analysis explores development patterns under varying gradient conditions. A back-propagation artificial neural network (BPANN) model identifies the primary driving factors influencing WEFE coordination. Key findings include the following: (1) the CCD of the PRD, HBOY, and CJP urban agglomerations has improved over time, with CCD values ranging between 0.4 and 0.6, 0.3 and 0.5, and 0.4 and 0.6, respectively. (2) The CCD exhibits a negative correlation with urbanization rates exceeding 70% and industrialization rates but shows a positive correlation with per capita GDP. (3) The dominant contributing subsystems vary; ecology in the PRD (28.76%), food in HBOY (28.83%), and food in CJP (29.32%). These findings underline the importance of tailored strategies for enhancing WEFE system coordination in urban agglomerations with diverse development gradients. Targeted policy recommendations are proposed based on regional characteristics and subsystem contributions.

Purpose: We phrase our analysis around the connectedness effects and portfolio allocation in the “Carbon-Energy-Green economy” system.Design/methodology/approach: This paper utilizes the TVP-VAR method provided by Antonakakis et al. (2020) and Chatziantoniou et al. (2021), and portfolio back-testing models, including bivariate portfolios and multivariate portfolios.Findings: Firstly, the connectedness within the “Carbon-Energy-Green economy” system is strong, and is mainly driven by short-term (weekly) connectedness. Notably, the COVID-19 pandemic leads to a vertical increase in the connectedness of this system. Secondly, in the “Carbon-Energy-Green economy” system, most of the sectors in the green economy stocks tend to be the transmitters of shocks to other markets (particularly the energy efficiency sector), while the carbon and energy markets are always the recipients of shocks from other markets (particularly the crude oil market). Thirdly, Green economy sector stocks have satisfactory hedging effects on the market risk of carbon and energy assets. Interestingly, hedging risks in relatively “dirty” assets requires more green economy stocks than in relatively “clean” assets. Finally, the results indicate that portfolios that include green economy stocks significantly outperform portfolios that do not contain green economy stocks, further demonstrating the crucial role of green economy stocks in this system.Originality/value: Understanding the interactions and portfolio allocation in the “Carbon-Energy-Green economy” system, especially identifying the role of the green economy performance in this system, is important for investors and policymakers.

Suaeda salsa (S. salsa) is an important ecological barrier and tourism resource in coastal wetland resources, and assessing changes in its health is beneficial for protecting the ecological health of wetlands and increasing finances. The aim was to explore improvements in the degradation of S. salsa communities in the Liao River Estuary National Nature Reserve since a wetland restoration project was carried out in Panjin, Liaoning Province, China, in 2015. In this study, landscape changes in the reserve were assessed based on Sentinel-2 images classification results from 2016 to 2019. A pressure-state-response framework was constructed to assess the annual degradation of S. salsa communities within the wetlands. The assessment results show that the area of S. salsa communities and water bodies decreased annually from 2016 to 2019, and the increased degradation indicators indicate a state of continued degradation. The area of types such as aquaculture ponds and Phragmites australis communities did not change much, while the estuarine mudflats increased year by year. The causes of S. salsa community degradation include anthropogenic impacts from abandoned aquaculture ponds and sluice control systems but also natural impacts from changes in the tidal amplitude and soil properties of the mudflats. The results also indicate that the living conditions of S. salsa in the Liao River estuary wetlands are poor and that anthropogenic disturbance is necessary to restore the original vegetation abundance.