Applied Spatial Analysis and Policy最新文献

{"title":"Towards Sustainable Transport: How Supply and Demand Factors Drive the Development of Railway Container Transport","authors":"Yifan Wang,&nbsp;Yumeng Wu,&nbsp;Zhizhen Bai,&nbsp;Sihan Li","doi":"10.1007/s12061-026-09873-5","DOIUrl":"10.1007/s12061-026-09873-5","url":null,"abstract":"<div>\u0000 \u0000 <p>Under China’s strategy for green transport transformation, “shifting from road to rail and bulk to container” is central to achieving dual-carbon goals and building a sustainable transport system. Railway container transport plays a key role, yet how supply and demand factors drive its growth requires systematic investigation. Based on operational data from China’s railway container handling stations, a multi-method analytical framework integrating random forest, spatiotemporal geographically weighted regression, and structural equation modelling has been established. This systematically reveals the driving mechanisms of supply and demand factors influencing railway container transport. Research findings indicate: (1) Key influencing factors exhibit significant heterogeneity across different city categories. Border cities are primarily driven by consumer demand, central hub cities are most sensitive to urban hierarchy factors, while coastal cities are most significantly influenced by port freight and terminal facilities. (2) Temporal dimension: The influence of site factors is diminishing, while the impact of domestic demand factors, represented by urban economic vitality, is increasing. Spatial dimension: Railway container transport in central and western regions exhibits significant dependence on the secondary industry. (3) Impact pathway: Consumer demand has begun to exert a driving influence; while the direct effect of further increasing road transport inputs remains marginal, indirect promotion of sustained rail container transport development persists through enhancing the supply system, fostering industrial synergy, and stimulating transport demand. These findings support differentiated policy-making for sustainable transport development.</p>\u0000 </div>","PeriodicalId":46392,"journal":{"name":"Applied Spatial Analysis and Policy","volume":"19 2","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial Pattern Evolution Analysis in Regional Economic Development Based on Long-Term NPP-VIIRS Nighttime Light Remote Sensing: A Case Study of Yunnan Province","authors":"Lingyan Bao,&nbsp;Yanan Li,&nbsp;Fan Zhang,&nbsp;Lei Lei,&nbsp;Xiuli Liu,&nbsp;Hua Pan,&nbsp;Zhen Zhang","doi":"10.1007/s12061-026-09875-3","DOIUrl":"10.1007/s12061-026-09875-3","url":null,"abstract":"<div>\u0000 \u0000 <p>Nighttime light (NTL) remote sensing provides a novel perspective for assessing the spatiotemporal evolution of socio-economic parameters across diverse regions, transcending the barriers of traditional statistical timelines and administrative regions. While previous studies have advanced NTL-based economic assessments, most have been limited to provincial or municipal administrative levels, lacking detailed evaluations at finer spatial scales and comprehensive analyses of long-term spatiotemporal economic trends. To address these gaps, this study utilized Yunnan Province in China as a case study to analyze the spatiotemporal dynamics and evolution of the province’s economy from 2013 to 2022, drawing on long-term NPP-VIIRS nighttime light remote sensing imagery. The suitability of various NTL remote sensing economic estimation models was explored in the study. The spatiotemporal characteristics of the economy in Yunnan Province were then evaluated using the optimal NTL remote sensing economic estimation model. Subsequently, Zipf’s law was utilized to evaluate regional economic development disparities and analyze the equilibrium of urban economic growth. The development trends at the pixel level in Yunnan Province from 2013 to 2022 were obtained based on remote sensing economic estimation results. Additionally, changes in linkage intensity between urban regions are analyzed and explained using Growth Pole Theory. The results indicated that the quadratic function model exhibits the strongest fit, achieving an Mean Absolute Error (MAE) value of 5.02 billion CNY. Urban development has exhibited a rapid expansion trend with significant economic growth, while county-level disparities have gradually diminished, indicating a trend towards more balanced development. Notably, Kunming, Yuxi, and Qujing showed significant improvements in both development trends and scales. The methodology proposed in this paper offers technical support for narrowing regional disparities and promoting high-quality, sustainable development.</p>\u0000 </div>","PeriodicalId":46392,"journal":{"name":"Applied Spatial Analysis and Policy","volume":"19 2","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How Open Data Reduces Carbon Emission Intensity Without Sacrificing Growth—Causal Evidence and Mechanism Identification Based on Double Machine Learning","authors":"Xiaolong Zhang,&nbsp;Sechu Aji,&nbsp;Rongxin Li,&nbsp;Wangcong Lilai,&nbsp;Yuanjie Deng","doi":"10.1007/s12061-026-09868-2","DOIUrl":"10.1007/s12061-026-09868-2","url":null,"abstract":"<div>\u0000 \u0000 <p>As global climate governance enters a phase of deep decarbonization, it remains unclear whether open access to public data—an essential component of digital governance—can effectively promote low-carbon development and serve as a viable policy instrument for achieving carbon neutrality. Using city-level data on public data openness in China, this study constructs a multi-period panel dataset and employs a Double Machine Learning (DML) framework to identify the net effect of public data openness on urban carbon emission performance, while controlling for high-dimensional covariates and potential endogeneity. The empirical results indicate that: (1) after multiple model specifications and robustness checks, public data openness is shown to significantly reduce urban carbon emission intensity, suggesting that it can act as an important driver of carbon neutrality progress; (2) mechanism analysis reveals that the effect operates through enhancing green technological innovation, optimizing the energy consumption structure, and lowering the growth rate of energy use; (3) heterogeneity analysis shows that the emission-reduction effect is more pronounced in regions northwest of the Hu Line, as well as in areas with higher vegetation coverage and stronger economic development. From a causal inference perspective, this study provides new empirical evidence on the role of public data governance in advancing carbon-neutral strategies and offers policy insights for building a high-quality data-factor market and promoting the coordinated transition toward digitalization and greening.</p>\u0000 </div>","PeriodicalId":46392,"journal":{"name":"Applied Spatial Analysis and Policy","volume":"19 2","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial Differentiation and Driving Mechanisms of Built Environment Resilience across the Urban-Rural Continuum: Evidence from a Megacity in Western China","authors":"Panyu Peng,&nbsp;Yibin Ao,&nbsp;Mingyang Li,&nbsp;Hong Xu,&nbsp;Hao Zhu,&nbsp;Bingjie Liu","doi":"10.1007/s12061-026-09870-8","DOIUrl":"10.1007/s12061-026-09870-8","url":null,"abstract":"<div>\u0000 \u0000 <p>Under the background of urban-rural integration, the spatial structure of regional systems has become increasingly complex. Existing studies mainly focus on cities as a whole or on individual communities, lacking systematic identification and comparative analysis across multiple spatial types. From the perspective of the built environment, this study constructs a spatial resilience evaluation system based on the three-dimensional framework of “robustness-resistance-recovery.” Taking Chengdu as a case study, five spatial units—main urban area, urban-rural fringe area, town center area, town-village transitional area, and village area are selected. A combined weighting method and ensemble modeling approach are employed to assess resilience levels and explore their driving mechanisms. The results show that: (1) built environment spatial resilience exhibits a distinct “core-transition-periphery” gradient pattern; (2) the dominant influencing factors vary significantly among spatial types, shifting from infrastructure-dominated to socio-ecological composite drivers; and (3) policy interventions should strengthen the recognition of regional heterogeneity and promote coordinated optimization of resource allocation and spatial governance across urban and rural areas. This study reveals the differentiation patterns and driving mechanisms of multi-type spatial resilience, providing theoretical support and practical guidance for coordinated urban-rural development and spatial governance optimization.</p>\u0000 </div>","PeriodicalId":46392,"journal":{"name":"Applied Spatial Analysis and Policy","volume":"19 2","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Conceived to Lived Space: a Multimodal Computational Investigation into the Digital Co-production of City Image","authors":"Jianing Zhu,&nbsp;Qiaoge Xie,&nbsp;Ruqin Ren","doi":"10.1007/s12061-026-09869-1","DOIUrl":"10.1007/s12061-026-09869-1","url":null,"abstract":"<div>\u0000 \u0000 <p>This study explores how city image is dynamically co-produced by constant negotiations between two forces: citizens’ bottom-up social media narratives and the top-down institutional mandates of public cultural spaces. We use Shanghai’s City Library and Party-mass Service Center as two examples. Focusing on how users articulate the spatial characteristics of public cultural spaces through textual and visual representations, this study employs an AI-driven multimodal approach to analyze large-scale data derived from Xiaohongshu, a Chinese lifestyle-oriented platform. Results show that both City Library and Party-mass Service Center content share convergent patterns with different emphases. Users’ representations consistently align with space functional logics and usage experiences. However, City Library-related content is predominantly shaped by lifestyle-oriented experiences, whereas Party-mass Service Center-related content is mainly characterized by administrative functions and public service provision. Users exhibit distinct sentiments that user-centered lived experiences are associated with positive sentiment and vibrant visual aesthetics, while governance-oriented content tends to elicit negative sentiment and is marked by clarity-driven, information-focused visual representations. Overall, the study concludes that city image is not a static presentation nor a mere reflection of urban planners’ intention but emerges from the interplay between user interpretations and institutional logics, with social media operating as a key infrastructural mediator.</p>\u0000 </div>","PeriodicalId":46392,"journal":{"name":"Applied Spatial Analysis and Policy","volume":"19 2","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Global (Dis)Similarity between Human Development and Demographic Reproduction and its Spatial Entropy from 1970 to 2020","authors":"Branislav Bleha,&nbsp;Pavol Ďurček","doi":"10.1007/s12061-026-09849-5","DOIUrl":"10.1007/s12061-026-09849-5","url":null,"abstract":"<div>\u0000 \u0000 <p>This study proves new manifestations of (dis)similarity between the levels of human development and demographic behavior. The primary research question is whether the set of populations of the world’s countries behaves demographically “in accordance” with the level of their human development. This seems like a trivial demographic question that has been addressed by a lot of demographers in the last decades. However, we think that a new perspective can still be brought to the topic. It turns out that the investigated relationship is not as crystal clear as it might seem. We have developed an original index of human development. Moreover, we dealt with a set of 195 countries as well as quite a long period − from 1970 to 2020. The first key finding is that human development and demographic behavior have become increasingly similar – more than expected; roughly twice as much in 2020 as in 1970. We also investigated whether people’s affiliation with respective world region has a changing contribution to the global variety of relationship between human development and demographic behavior. The main finding is that the contributions of spatial dimensions (continents, macro-regions, countries) to mutual dissimilarity between human and demographic developments change over time.</p>\u0000 </div>","PeriodicalId":46392,"journal":{"name":"Applied Spatial Analysis and Policy","volume":"19 2","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12061-026-09849-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial Heterogeneity of the Impact of New Quality Productive Forces on Carbon Emissions in Chinese Urban Agglomerations","authors":"Yilong Li,&nbsp;Chengzu Wang,&nbsp;Liqun Sun,&nbsp;Zhengguo Li","doi":"10.1007/s12061-026-09836-w","DOIUrl":"10.1007/s12061-026-09836-w","url":null,"abstract":"<div>\u0000 \u0000 <p>This study examines how New Quality Productive Forces (NQPF) and other factors are associated with carbon emissions across 19 national urban agglomerations and 211 prefecture-level cities in China from 2010 to 2022. Conceptually, NQPF is interpreted as a context-specific form of production system transformation, integrating innovation-led upgrading, digital transformation, and green technological progress under China’s “dual carbon” goals. The framework posits that decarbonization depends on whether innovation-led substitution effects outweigh scale and rebound effects under heterogeneous regional structural conditions. Using kernel density estimation (KDE), Geographic Detectors (GeoDetector), and Geographically and Temporally Weighted Regression (GTWR), the analysis reveals pronounced spatiotemporal heterogeneity and interaction patterns in emission drivers. Results show that economic growth (PGDP) and NQPF are positively associated with emissions in many regions, consistent with scale and rebound effects during early-stage transformation, whereas green technology innovation (GTI) is consistently linked to emission mitigation with increasingly uniform spatial effects over time. Urbanization exhibits a nonlinear relationship, with its mitigation potential strengthening as infrastructure efficiency and technology diffusion improve. Interaction detection further indicates that most factor pairs display enhancement effects, suggesting that emission outcomes are shaped by coupled mechanisms between carbon intensity, technological upgrading, and development context. Based on these patterns, urban agglomerations are classified into four production system transformation pathways - substitution-led, catch-up, locked-in, and growth-first substitution-later - each exhibiting distinct decarbonization trajectories. Policy implications emphasize pathway-specific strategies that align technological innovation, industrial restructuring, and energy transition with local structural conditions. Overall, the findings provide spatiotemporally explicit evidence for differentiated low-carbon transition pathways across China’s urban agglomerations.</p>\u0000 </div>","PeriodicalId":46392,"journal":{"name":"Applied Spatial Analysis and Policy","volume":"19 2","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147737704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial Characteristics and Influencing Factors of Traditional Villages in the Grand Canal Basin, China: The Interconnection Between Rural Revitalization and Tourism Development","authors":"Ge Shi,&nbsp;Ziying Feng,&nbsp;Jingran Zhang,&nbsp;Chuang Chen,&nbsp;Jinghai Xu,&nbsp;Yunpeng Zhang,&nbsp;Xinyu Li,&nbsp;Zhuang Tian","doi":"10.1007/s12061-026-09857-5","DOIUrl":"10.1007/s12061-026-09857-5","url":null,"abstract":"<div>\u0000 \u0000 <p>Rural areas and tourism are closely interconnected. The revitalization and development of rural areas rely on the promotion of tourism, while the prosperity of the tourism industry depends on the support of rural areas. This study examines the spatial distribution patterns and influencing factors of 508 national-level traditional villages within the Grand Canal Basin. Employing kernel density estimation, spatial autocorrelation, cluster and outlier analysis, and standard deviation ellipse methods, the research identifies key spatial patterns of these villages. Additionally, the study utilizes geographical detector and geographically weighted regression (GWR) models to reveal the spatial heterogeneity of factors shaping their distribution and analyze the impact of various factors on the development of village tourism. The findings reveal: (1) The distribution of traditional villages in the Grand Canal Basin exhibits significant spatial imbalance, with central, southern, and parts of the northern regions serving as major aggregation zones; (2) The traditional villages exhibit a “one-belt, multi-core” distribution pattern and show a notable positive spatial autocorrelation; (3) Key factors influencing the distribution include the proportion of forest land, average annual precipitation, average slope, average sunshine, and road density, with average slope emerging as the most influential factor. Various factors also affect the development of village tourism to differing degrees, with market potential having the greatest impact.</p>\u0000 </div>","PeriodicalId":46392,"journal":{"name":"Applied Spatial Analysis and Policy","volume":"19 2","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147737633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Interplay Between Environmental Factors and COVID-19: Evidence from India’s District-Level Data","authors":"Suryakant Yadav,&nbsp;Pravat Bhandari","doi":"10.1007/s12061-026-09853-9","DOIUrl":"10.1007/s12061-026-09853-9","url":null,"abstract":"<div>\u0000 \u0000 <p>This study contributes to the existing body of literature examining the role of environmental factors on COVID-19 disease outcomes. In essence, the study aimed to comprehensively assess the global and local (i.e., spatially varying) associations of COVID-19 infections and related deaths with environmental factors across small-scale geographies in India. We compiled a district-level database that includes: two COVID-19 disease outcomes measuring infection rates and case fatality rates; six environmental variables capturing meteorological conditions, air pollution concentrations, and environmental greenness; and nine potential covariates reflecting demographic, socioeconomic, and population health characteristics. Furthermore, we employed ordinary least squares (OLS) regression and geographically weighted regression (GWR) models to explore the global and local relationships, respectively, between COVID-19 disease outcomes and environmental factors, while controlling for the effects of covariates. Our findings revealed that the concentration of two air pollutants, PM2.5 and NO2, and exposure to green space significantly influenced the COVID-19-related outcomes. We found that elevated levels of PM2.5 and NO2 were independently associated with increased COVID-19 incidence rates (IR) and higher COVID-19 case fatality rates (CFR). Conversely, greater exposure to environmental greenness was significantly associated with lower COVID-19 CFR. Our results also emphasise substantial geographical heterogeneities in these relationships across districts. These results imply that residing in greener areas, as well as in locations with lower air pollution levels, may confer significant health benefits in preventing the adverse impacts of COVID-19.</p>\u0000 </div>","PeriodicalId":46392,"journal":{"name":"Applied Spatial Analysis and Policy","volume":"19 2","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147737536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revealing the Heterogeneous Regional Effects of Macroeconomic Policies: Evidence from a CS-ARDL Approach","authors":"Mohammed Algoni,&nbsp;Mehmet Ivrendi","doi":"10.1007/s12061-026-09862-8","DOIUrl":"10.1007/s12061-026-09862-8","url":null,"abstract":"<div>\u0000 \u0000 <p>This study examines how national monetary and fiscal policies affect Turkey’s 26 NUTS-2 regions differently in terms of income, inflation, and unemployment from 2004 to 2022. This study makes three key contributions. First, we extend the existing regional literature by jointly analyzing how monetary and fiscal policies are transmitted in the long term, revealing notable differences between industrialized Western regions and less developed regions in the interior and east. Second, we develop a quarterly regional panel from annual data using temporal disaggregation, which meets the requirements for second-generation panel data analysis. This allows us to rigorously account for cross-sectional dependence arising from common global and national shocks (e.g., currency fluctuations, inflation spikes), ensuring our model selection process is robust. This process confirms that CS-ARDL is suitable for our mixed integration levels, cointegration, and cross-sectional dependence diagnostics with CD-free residuals after estimation. Finally, our empirical analysis reveals that long-term fiscal expansion is associated with lower regional per-capita incomes, suggesting crowding-out effects. In contrast, monetary easing has limited real effects in less-developed regions. However, it significantly increases unemployment in the most industrialized areas, supporting capital-dependent and reallocation mechanisms in the long run. These findings suggest that uniform national stabilization policies may exacerbate regional disparities, necessitating the implementation of tailored regional policy adjustments. Our research advances the study of macro-regional policy transmission by jointly estimating fiscal and monetary channels within a unified second-generation panel framework, mapping their diverse, long-term, and heterogeneous impacts.</p>\u0000 </div>","PeriodicalId":46392,"journal":{"name":"Applied Spatial Analysis and Policy","volume":"19 2","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147643040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}