Clarifying the residential space differentiation in urban fringe areas and its influencing factors are crucial for land use planning and sustainable urban development. This study investigates residential space differentiation and its influencing factors in the urban fringe area of Beijing from the perspective of housing rent. Utilizing multi-source data, including housing rent statistics from the China Real Estate Price Platform, remote sensing imagery, and POI big data, we employ the residential dissimilarity index for tenants, geographical detector, and MGWR model to analyze spatial patterns and driving mechanisms. The results show the following: (1) The residential space differentiation in the urban fringe area of Beijing is obvious, showing an “X”-shaped fragmentation pattern, with the northeast and southwest regions forming high differentiation values, while the northwest and southeast regions form low differentiation values. (2) The residential space differentiation in the marginal area shows a strong scale effect, which originates from the historic “collage” development mode of Beijing. (3) The differentiation of residential space in Beijing’s urban fringe area is sensitive to the spatial accessibility of residential areas to other facilities, and is less affected by the spatial proximity, such as the number of facilities. (4) The central potential and traffic potential factors are still the core driving forces shaping the differentiation pattern of residential space in the marginal area; the role of leisure supporting factors has become increasingly prominent, and it has gradually become the key factor strengthening residential space differentiation; and the influence of medical and commercial supporting factors is relatively weak.

Property-rights confirmation and registration are widely regarded as an institutional foundation for ecological-product value realization, yet the dynamic mechanism through which registration clarity affects multi-actor cooperation remains insufficiently understood. Using the Yunhe Terraced Fields National Wetland Park in Zhejiang Province as a case, this study develops a tripartite evolutionary-game model involving the local government, farmers, and tourism enterprises. The model incorporates registration clarity into the payoff structure through transaction-cost reduction, compensation realization, and regulatory constraint channels. Parameters are calibrated using gross ecosystem product (GEP) accounting, policy documents, administrative records, and field survey data. Numerical simulations show that clearer registration expands the basin of attraction of cooperative equilibrium and accelerates convergence under the calibrated parameter setting. Government incentives serve as an important external trigger, enterprise participation depends on a feasible balance between cost-sharing and expected returns, and farmers’ ecological protection is crucial for maintaining long-term cooperative stability. The study contributes by transforming registration clarity from a descriptive institutional background into a scenario-based institutional-intensity variable within an evolutionary-game framework. The findings should be interpreted as calibrated simulation evidence rather than direct causal estimates, and they suggest an integrated institutional path linking registration, compensation, transaction-cost reduction, and regulation.

This paper investigates the adoption strategies of blockchain technology within the framework of a dual-channel supply chain. Applying game theory, we demonstrate that the adoption decision of supply chain members depends on consumer privacy concern. Furthermore, we derive an equilibrium in which one chain member adopts blockchain technology while the other does not, depending on consumer acceptance of the online channel and transportation costs. Additionally, we identify an equilibrium where both chain members adopt blockchain technology, which may lead to either a win–win or a win–lose outcome for the manufacturer and retailer, respectively. Our findings also suggest that consumer surplus increases as consumer privacy concern grows. Moreover, three extensions analytically confirm the robustness of our results.