Applied Spatial Analysis and Policy最新文献

This study proposes a multiscale evaluation framework for recreation services (RS) based on the Environment–Opportunity–Potential (EOP) model, aiming to better understand the dynamics and drivers of RS across spatial scales. Using the Yangtze River Delta (YRD) region of China as a case study, this research assesses the RS levels from 2010 to 2020 based on the Environment Opportunity and Potential (EOP) framework. We investigated the trade-offs and synergies among multiple dimensions of RS and analyze their driving mechanisms at varying spatial scales. Key findings include: (1) The YRD exhibits a distinct spatial pattern characterized by higher values in the southeastern areas and lower values in the northwest, and the average level decreased by 5.85% over the past decade. (2) The direction and magnitude of trade-offs and synergies among different RS dimensions are scale-dependent, with spatial scale exerting a more pronounced influence on these interactions than temporal scale. (3) RS exhibited significant spatial clustering across all scales, with High–High clusters primarily located in southern Jiangsu, Shanghai, and Zhejiang. These hotspots showed strong spatial associations with transportation corridors, though their dependence on urban centers declined over time. (4) Socioeconomic factors play a critical role in shaping RS, but their effects vary significantly between grid and administrative scales in both direction and magnitude. Based on these findings, we propose a multiscale RS management framework (city-county-grid) to guide spatial planning and policy formulation. This framework helps align RS development with local contexts and supports sustainable recreational planning in rapidly urbanizing regions.

The 4.0 technologies provide a technological window for regions to promote their innovation level and achieve leapfrogging. Using the panel data of China’s cities in the period from 2004 to 2021, this study finds the significantly positive impacts of technological window in technological innovation and technological catch-up and that these impacts are amplified by knowledge spillovers. Dividing regions into complex regions and simple regions based on their knowledge complexity, the roles of technological window and knowledge spillovers show heterogeneity. Complex regions are more sensitive to utilize technological window and knowledge spillovers to accelerate innovation and catch-up, while the technological window is less efficient for simple regions and these regions are difficult to absorb frontier knowledge spillovers.

The rapid increase in the number of older migrants in China has highlighted the need for a nuanced and comprehensive understanding of the factors influencing their settlement intentions. However, previous research on older migrants’ settlement intention in China tends to neglect the role of air pollution. Using data from the 2017 and 2018 China Migrants Dynamic Survey (CMDS), this study examines whether the severity of air pollution affects older migrants’ intentions to settle in the destination city. The research presents two contributions to the literature. First, we provide the first systematic evidence on how air pollution shapes older migrants’ settlement decisions in host cities. Second, in response to the disparities in health status and adaptive capacities observed between older migrants and working-age groups, we develop age-specific policies designed to address their elevated vulnerability profiles. Findings demonstrate that urban air pollution adversely impacts older migrants’ settlement intentions, with each one-unit rise in the Air Quality Index (AQI) reducing their Likelihood of permanent settlement by 0.1% points. Additionally, our research reveals that older migrants with agricultural hukou status, higher educational attainment, lower income, longer migration duration, and less exposure to air pollution in their hometowns are more vulnerable to air pollution’s negative impacts.

Land use plans are closely associated with landscape changes. Since the 1990s, China has implemented quota-based farmland protection planning to attenuate farmland loss and ensure food security. However, existing literature has primarily focused on compliance with planning blueprints, rather than on the varying impacts of planning on farmland use and underlying mechanisms. To bridge this gap, this study argues that China’s farmland protection planning was implemented as a “soft constraint”, with its impacts moderated by economic forces. This study examined this hypothesis by taking National General Land Use Plan (2006–2020) as a case study. Employing a two-way fixed-effects model, robustness checks and heterogeneity analysis, this study convincingly assessed the plan’s effectiveness by comparing outcomes across prefectures with varying protection targets and different economic driving forces. Results suggest that the planning significantly reduced farmland loss by aligning with economic forces, although farmland area quotas were not fulfilled either nationwide or in most prefectures. Moreover, the planning was more effective in regions with higher benefits and lower opportunity costs of plan implementation, thereby amplifying the spatial shifts of farmland. These findings deepen the understanding of what determines planning effectiveness, helping researchers and policymakers evaluate the implementation and spatial variance of other land use policies.

The rapid proliferation of ride-hailing services has fundamentally reshaped urban mobility landscapes, challenging the operational paradigms of traditional taxi industries. While existing literature extensively explores the spatial and temporal patterns of ride-hailing, critical gaps persist in understanding the granular differences in daily travel behaviors between these two modes. This study addresses this gap through a data-driven analysis of traditional and ride-hailing taxis in Jinan, China, leveraging high-resolution spatial-temporal origin-destination (OD) datasets. By employing geostatistical modeling and efficiency metrics, we systematically quantify disparities in service coverage, trip distribution dynamics, and operational efficiency across six days of continuous observation. Results reveal that ride-hailing services not only double the trip volume of traditional taxis but also exhibit superior spatial adaptability, extending coverage to peripheral urban areas with a greater service radius. Temporal analysis reveals ride-hailing’s optimized resource allocation, characterized by lower idle time during off-peak hours compared to traditional counterparts. Efficiency assessments indicate that traditional taxis contribute more to inefficient travel, often replacing non-motorized transport modes. This inefficient travel mainly comes from unplanned trips to the city center for leisure activities. These findings provide insights into integrated mobility systems that harness ride-hailing’s spatial flexibility, supporting empirical study for more efficient urban transport planning.

The paper studies the dynamics of agricultural land use at the global scale as measured from space using satellite imagery between 2003 and 2018. It shows large global movements in and out of cropland and correlates these movements with biophysical, economic, and institutional variables. The empirical identification of these effects relies on a two-stage approach that disentangles the effect of local geography from national-level characteristics. Beyond the anticipated influence of local geographic factors (such as droughts leading to land degradation) or market access (such as proximity to cities facilitating cropland expansion), this study makes a substantial contribution by demonstrating the significance of national economic contexts and institutions. In particular, the research finds that income inequality, weak land administrations, land tenure insecurity, a bad business environment, and pressure on land resources are closely associated with cropland loss and land degradation.

This paper examines the revealed preferences for the composition of local public expenditures. It analyzes how the combined system of local governments in US cities may attract people to move to their jurisdiction, captured by change in population and housing prices. The paper examines the complete local public fiscal composition, considering the constrained choice set, in which each expenditure decision affects the rest of the fiscal composition. Furthermore, it makes an explicit distinction between capital outlay and current operations expenditures. The results show that change in population and housing prices are complementary in capturing revealed preferences. Although they both suggest a preference for lower spending/taxes, and allocating spending towards parking facilities and investments in highways, they also reveal different preferences for the allocation of the expenditures. Expenditures on higher education are associated with population growth, whereas expenditures on airports are associated with higher housing prices.

The United Nations’ primary Sustainable Development Goal (SDG) aims to eradicate extreme poverty in all its forms by 2030. Traditionally, poverty has been measured using monetary metrics; however, the Oxford Poverty and Human Development Initiative (OPHI) introduced the Multidimensional Poverty Index (MPI) to provide a more comprehensive perspective. Despite its importance, research on multidimensional poverty remains limited, particularly at the community level. This study utilizes data from the 5th round of the National Family Health Survey (NFHS) to examine the specific deprivations and spatial variations of multidimensional poverty across India, with a focus on social category specific disparities. The findings reveal that Scheduled Tribes (ST) and Scheduled Castes (SC) experience the highest levels of deprivation in health, education, and living standards, with ST category facing the most severe challenges. Despite various government initiatives, a significant proportion of tribal households still lack access to clean cooking fuel and adequate housing. Logistic regression analysis identifies key determinants of multidimensional poverty, including household head characteristics, household size, and factors such as the presence of young children, tuberculosis, and disability. Additionally, decomposition analysis highlights wealth, education, and health as the most significant contributors to multidimensional poverty. These findings highlight persistent inequalities affecting marginalized social categories and emphasize the need for targeted policy interventions. Implementing social category-specific strategies is crucial for addressing disparities and ensuring that poverty eradication efforts align with the SDGs.

Spatio-temporal patterns of tourist flow can effectively illustrate the movements of tourists and play an important role in guiding the planning and management of tourist destinations. This study proposes a novel multi-scale analytical framework that combines spatio-temporal analysis and social network analysis to examine these patterns in Tibet, based on digital footprint data. Results revealed that tourist flows in Tibet exhibit a distinct hub-and-spoke pattern, characterized by high-frequency corridors connecting major destinations. Seasonal fluctuations are notable, with visitor volumes peaking in the third quarter and declining sharply during winter, especially in remote areas. Social network analysis identified prominent core nodes, Potala Palace, Namtso Lake, and Barkhor Street, serving as central hubs with strong radiative influence. In contrast, edge nodes attract more specialized tourist segments. This core–periphery structure underscores the spatial imbalance in tourism resource distribution, offering critical insights for targeted regional planning and balanced tourism development. These insights have practical applications in tourism planning in Tibet, thereby providing indispensable support for the sustainable development of tourism in plateau regions.