Applied Spatial Analysis and Policy最新文献

{"title":"Unraveling Nonlinear and Spatial Heterogeneity Effects of Industrial Water Use Efficiency: Evidence From 41 Cities in Northeast China","authors":"Caizhi Sun,&nbsp;Mingyi Wang,&nbsp;Shuai Hao","doi":"10.1007/s12061-026-09951-8","DOIUrl":"10.1007/s12061-026-09951-8","url":null,"abstract":"<div><p>Identifying the key influencing factors of IWUE and formulating industrial water-saving policies are crucial for promoting green transformation and upgrading in the region. Existing studies mostly employ linear models to analyze the influencing factors of IWUE, which struggle to capture complex nonlinear relationships, interaction effects, and spatial heterogeneity among variables, resulting in an insufficient understanding of the underlying mechanisms. To address these gaps, this study uses panel data for 41 prefecture-level cities (leagues) in Northeast China from 2000 to 2022. It integrates the Super-SBM model, interpretable machine learning methods, and GTWR to construct an analytical framework that systematically reveals the spatiotemporal patterns, nonlinear driving mechanisms, and spatial differentiation characteristics of IWUE. Key findings show that IWUE increased from 0.25 in 2000 to 0.63 in 2022, rising from a medium-low to a medium-high efficiency level, with cumulative growth of 152%. Spatially, the region has shifted from being dominated by medium-low efficiency cities to one primarily composed of medium-high and high efficiency cities. Among all influencing factors, the IUI is the most critical and consistently positive driver, exhibiting an almost monotonically increasing effect. Furthermore, significant synergistic effects exist among the various factors. The magnitude and direction of each factor’s influence exhibit clear spatial heterogeneity, shaped primarily by local industrial structure, development stage, and resource endowment.</p></div>","PeriodicalId":46392,"journal":{"name":"Applied Spatial Analysis and Policy","volume":"19 3","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148614591","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":"AI-Driven Housing Affordability Forecasting in New York City: An NTA-Level Panel Analysis using Ensemble Machine Learning","authors":"Azizur Rahman,&nbsp;Nakib Uddin Ahmed,&nbsp;Mehjabin Ferdous,&nbsp;Kadirur Rahman Chowdhury","doi":"10.1007/s12061-026-09955-4","DOIUrl":"10.1007/s12061-026-09955-4","url":null,"abstract":"<div><p>Housing affordability is one of the signature governance challenges in New York City (NYC), with more than 52% of renter households cost-burdened and just under a third identified as severely cost-burdened, paying above 50% of gross income on housing. This study proposes a neighborhood-scale, explainable machine learning framework to predict severe housing cost burden at the Neighborhood Tabulation Area (NTA) level, drawing on a panel of 2,512 NTA-year observations spanning 239 NTAs from 2012–2022. Thirty modelling features derived from 49 raw socio-economic, housing market, and rent index variables are integrated from ACS 5-year estimates, eviction court records, and the Zillow Observed Rent Index (ZORI). Three gradient-boosted ensemble models—Random Forest, XGBoost, and LightGBM are benchmarked under a strict temporal train/validation/test split with 5-fold TimeSeriesSplit cross-validation to prevent data leakage. LightGBM achieved the highest predictive performance (Test <span>(R^2)</span>=0.9302, RMSE=0.03372, MAE=0.0185). SHAP TreeExplainer analysis identifies rent burden, vacancy rate, and rent-to-income ratio as dominant drivers, confirming that NYC’s rent escalation follows a systemic “burden escalator” across boroughs. Spatial diagnostics confirm no significant residual autocorrelation at both the borough level (Moran’s <i>I</i>=−0.0731, <i>p</i>=0.083) and NTA level (<i>I</i>=0.0204, <i>p</i>=0.238, <i>k</i>=5 kNN, <i>n</i>=239 NTAs), and ablation analysis confirms rental market variables drive the largest incremental contribution (<span>(Delta R^2)</span>=0.1629, 17.51%). A 8-instrument policy matrix is proposed, encompassing early-warning interventions, borough-differentiated zoning and voucher strategies, and anticipatory resource allocation. This framework demonstrates how explainable AI can operationalize predictive insights into actionable urban governance strategies, offering a replicable model for global cities facing housing crises.</p></div>","PeriodicalId":46392,"journal":{"name":"Applied Spatial Analysis and Policy","volume":"19 3","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148614117","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 Efficiency to Equity: Assessing Supply–Demand Fairness in Underground Pedestrian System Safety in Japanese Cities","authors":"Zongchao Gu,&nbsp;Haipeng Zhu,&nbsp;Ruizhe Liu,&nbsp;Tianyue Wan","doi":"10.1007/s12061-026-09950-9","DOIUrl":"10.1007/s12061-026-09950-9","url":null,"abstract":"<div><p>Underground Pedestrian Systems (UPSs) are important components of urban resilience, yet existing safety assessments often remain efficiency-oriented and insufficiently address equity and vulnerability. This study develops an integrated supply-demand-equity framework to evaluate UPS safety from a disaster risk reduction perspective. Combining geospatial, socio-demographic, and infrastructural data, the framework constructs the Safety-support Supply Index (SSI), Safety Demand Index (SDI), Supply-Demand Mismatch Index (SDMI), Socially Weighted Demand Equity Index (SDEI), and Group Equity Index (GEI). Four typologically contrasting Japanese UPSs, namely Tokyo, Shinjuku, Sapporo, and Tenjin, are examined to diagnose network-based safety-support adequacy and distributive fairness. The results reveal pronounced spatial and social heterogeneity. Sapporo shows the highest relative supply adequacy and composite equity performance (GEI = 1.071), whereas Tenjin exhibits combined deficits in safety-support supply and equity performance (GEI = 0.453). Shinjuku shows a potential distributional equity deficit despite moderate safety-support capacity, while Tokyo presents a near-threshold equity profile. These contrasts suggest that built-environment conditions and governance arrangements may be associated with different safety-equity outcomes across UPS types, although the findings should be interpreted as exploratory comparative evidence rather than causal or statistically generalizable results. The proposed framework translates spatial justice into operational diagnostic indicators and provides a transparent tool for identifying supply–demand mismatch and group-sensitive safety deficits in subterranean urban systems. It is intended as an exploratory spatial-equity diagnostic framework rather than a hazard-specific engineering safety audit.</p></div>","PeriodicalId":46392,"journal":{"name":"Applied Spatial Analysis and Policy","volume":"19 3","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148614368","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":"Scale-Dependent and Non-linear Effects of Land-Cover Configuration on Carbon Performance: an MGWR-SHAP Analysis of Tianjin","authors":"Jiaxiang Wang,&nbsp;Tian Chen","doi":"10.1007/s12061-026-09954-5","DOIUrl":"10.1007/s12061-026-09954-5","url":null,"abstract":"<div><p>Understanding how land-cover configurations influence spatial carbon performance (SCP) is critical, yet existing studies often overlook the scale-dependent and nonlinear threshold effects across urban–rural gradients. Taking Tianjin as a case study, we construct a four-tier gradient framework. By coupling Multi-scale Geographically Weighted Regression (MGWR) with the LightGBM-SHAP machine learning approach, this study decodes the spatially heterogeneous, nonlinear, and interactive mechanisms driving SCP. The results reveal a fundamental shift in land-cover impacts from the urban core to the rural periphery. Crucially, we identify explicit morphological and ecological thresholds: in high-density urban cores, building density exhibits a pronounced inverted-U response, with SCP peaking only when density ranges between 2.5 and 3.5, beyond which heat-island penalties and energy consumption outweigh agglomeration benefits. Conversely, in rural and peri-urban zones, ecological drivers dominate; notably, green-space coverage demonstrates a critical threshold at 15%, beyond which its marginal contribution to SCP shows a substantial synergistic increase. Furthermore, green-space connectivity triggers significant carbon-sink benefits once exceeding an index of 0.45. Furthermore, complex interactions, such as the coupling of road network density with topographic variability and functional diversity, are quantified. These findings advance the transition from \"one-size-fits-all\" emission policies to gradient-specific, threshold-guided spatial optimization, offering actionable evidence for sustainable territorial governance.</p></div>","PeriodicalId":46392,"journal":{"name":"Applied Spatial Analysis and Policy","volume":"19 3","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148614492","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":"Understanding the Association Between Neighbourhood Type, Built Environment, and Overweight in China: Evidence From Hebei Province","authors":"Mingjie Sheng,&nbsp;Longmei Tang,&nbsp;Wen Jiang,&nbsp;Lei Yang,&nbsp;Peixuan Chen","doi":"10.1007/s12061-026-09953-6","DOIUrl":"10.1007/s12061-026-09953-6","url":null,"abstract":"<div><p>The uneven distribution of overweight across urban areas represents a form of spatial health inequality. In contrast to Western countries identifying two types of overweight neighbourhoods, neighbourhood type in China may act as a proxy connecting socioeconomic stratification, built environment disparities and overweight, shaped by both institutional and market forces. Despite this complexity, the relationship remains underexplored. This study examines the associations among neighbourhood type, built environment factors, and overweight status, drawing on data from 1,542 adults across 42 urban neighbourhoods in Hebei Province, China. Six logistic regression models were used to assess how obesogenic environmental factors influence overweight and how these effects differ across neighbourhood types. Results show that higher densities of unhealthy food retailers, greater green space, and higher land use mix are associated with an increased risk of being overweight. The influence of the built environment varies by neighbourhood type: land use mix is more impactful in old communities, while food environment and green space are more influential in commodity housing estates. Additionally, gender differences in these associations are observed only among residents of old communities. Although overweight prevalence is high in China, no spatial disparity is observed between the two neighbourhood types. Nonetheless, built environment elements influence overweight in nuanced ways, both in comparison with Western contexts and between China’s old communities and commodity housing estates. This paper offers new insight into how China’s unique institutional residential structure contributes to spatial patterns of overweight through the interaction between built environment elements and residents’ socio-demographic characteristics.</p></div>","PeriodicalId":46392,"journal":{"name":"Applied Spatial Analysis and Policy","volume":"19 3","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148613632","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":"Spatiotemporal Gradient Evolution and Driving Mechanisms of Ecological Resilience in the Yangtze River Economic Belt: A Multi-Scale Analysis","authors":"Ruzi Li,&nbsp;Xuanpeng Tang,&nbsp;Sixian Meng,&nbsp;Weifeng Deng,&nbsp;Guoen Wei,&nbsp;Chenghao Liu,&nbsp;Yaobin Liu","doi":"10.1007/s12061-026-09943-8","DOIUrl":"10.1007/s12061-026-09943-8","url":null,"abstract":"<div><p>Enhancing ecological resilience is critical for promoting regional sustainable development and green transformation. However, the specific gradient evolution patterns of ecological resilience in large-scale basins and the underlying driving mechanisms remain insufficiently understood. This research establishes a comprehensive ecological resilience assessment model based on the “resistance-adaptability-recovery” framework. Utilizing multi-source remote sensing data linked to land use information, this study examines the spatiotemporal evolution of the ecological resilience gradient across 1,070 counties in the Yangtze River Economic Belt (YREB) and investigates the mechanisms driving this evolution. The results show that ecological resilience in the YREB remained relatively low and followed a modest decline–rebound trajectory from 2000 to 2020, decreasing by 2.17% between 2000 and 2010 and subsequently increasing by 1.90% between 2010 and 2020. At the basin scale, the longitudinal and latitudinal gradients remained generally stable, with ecological resilience decreasing toward higher longitudes and latitudes. At the urban agglomeration scale, ecological resilience in the Middle Reaches of the Yangtze River Urban Agglomeration (MRYRU) was 8.64% and 23.70% higher than that in the Yangtze River Delta Urban Agglomeration (YRD) and Chengdu–Chongqing Urban Agglomeration (CCU), respectively. From the core to the periphery, the three urban agglomerations exhibited distinct “decline–increase”, “decline”, and “increase” gradient patterns. Comparisons across scales indicate that basin-wide geographical differences provide the broad spatial context for ecological resilience, while regional urbanization further reshapes the basin-wide gradient within urban agglomerations. The GeoDetector and geographically and temporally weighted regression (GTWR) analyses further reveal that the factors influencing ecological resilience gradually shifted from natural-background factors, represented by forest coverage, toward human-related factors, represented by industrial structure and population density. Forest coverage exerted an overall positive effect, whereas the direction and magnitude of the effects of industrial structure and population density varied substantially across space. These findings reveal the multi-scale mechanisms through which basin-scale natural gradients and urbanization within urban agglomerations jointly shape the spatial pattern of ecological resilience, providing a scientific basis for tiered and differentiated resilience governance in the YREB.</p></div>","PeriodicalId":46392,"journal":{"name":"Applied Spatial Analysis and Policy","volume":"19 3","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148614001","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":"Mapping Hemoglobin Levels Among Women and Children Across Ethiopian Administrative Zones: A Geospatial Small Area Estimation Approach","authors":"Seyifemickael Amare Yilema,&nbsp;Najmeh Nakhaei Rad,&nbsp;Ding-Geng Chen","doi":"10.1007/s12061-026-09949-2","DOIUrl":"10.1007/s12061-026-09949-2","url":null,"abstract":"<div><p>Hemoglobin, an iron-rich protein in red blood cells, enables oxygen delivery to all body tissues. The hemoglobin records in most countries are collected through surveys. Surveys often provide reliable estimates of target variables for the population at both national and regional administration levels. This study investigates the spatial distribution of hemoglobin levels among women and children across Ethiopian administrative zones. Because survey sample sizes are often insufficient to produce reliable direct estimates at the zonal level, small area estimation methods are used to improve the precision of estimates. Small area estimation provides reliable estimates at the local zones by combining survey, census, and spatial datasets. Therefore, we focus on geospatial small area estimation to provide reliable and precise hemoglobin estimates for women and children. Therefore, our prime objective is to provide reliable and precise estimates of hemoglobin levels for women and children using geospatial small area estimation by integrating the survey and census datasets. An area-level Fay-Herriot modeling framework was implemented to estimate hemoglobin levels across the studied zones. The results show that for both children and women, the Spatial Empirical Best Linear Unbiased Predictor (SEBLUP) consistently produces the lowest coefficients of variation (CVs), followed by the standard Empirical Best Linear Unbiased Predictor (EBLUP), while direct survey estimates display the highest variability. The comparison of CVs for hemoglobin estimates demonstrates that children’s mean CVs decrease from 13.64 (Direct survey estimates) to 11.29 (EBLUP) and then to 6.06 (SEBLUP). Similarly, women’s mean CVs decline from 13.48 (Direct survey estimates) to 10.71 (EBLUP) and then 6.37 (SEBLUP), confirming that SEBLUP provides the highest precision for both children and women, consistently producing the lowest CVs across all statistical measures. The results in the maps indicate the spatial disparities in hemoglobin levels for both women and children among the Ethiopian administrative zones. Policymakers will significantly benefit from these local area-level estimates, and researchers will gain methodological insights into the application of geospatial small-area estimation.</p></div>","PeriodicalId":46392,"journal":{"name":"Applied Spatial Analysis and Policy","volume":"19 3","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12061-026-09949-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148613891","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":"Temporal Changes in Human Perception of Street Space: A Street View Images-Based Analysis","authors":"Rui Xiao,&nbsp;Yang Zhou,&nbsp;XingJian Liu,&nbsp;Qiuping Li","doi":"10.1007/s12061-026-09952-7","DOIUrl":"10.1007/s12061-026-09952-7","url":null,"abstract":"<div><p>While multi-year street view images (SVIs) are increasingly applied to infer human perception of the built environment, it remains challenging to efficiently identify perception changes over time. To this end, this paper introduces a novel end-to-end Perception Changes Assessment Siamese Convolutional Neural Network (PCAS-CNN) model. The model leverages multi-year SVIs and requires a small training sample set, identifying intuitive and temporal variations in human perception of street space. The analysis subsequently explores associations between perception changes and socio-economic factors. A case study in Wuhan, China shows that the proposed PCAS-CNN model achieves a high accuracy and effectively identifies three types of perception changes, i.e., better, unchanged, and worse. The results suggest that Point of Interest (POI) intensity, house prices, urban vitality, and nighttime light intensity changes are significantly associated with perception changes. Notably, the relevance of these factors varies across different regions and points to uneven patterns of urban development. The developed method enables the identification of perception changes with a small annotation dataset, thus offering support for city-wide applications.</p></div>","PeriodicalId":46392,"journal":{"name":"Applied Spatial Analysis and Policy","volume":"19 3","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148613862","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":"Intelligent Assessment of Urban Renewal Potential and the Nonlinear Relationships with Landscape Elements: Integrating Large Language Models with Interpretable Machine Learning","authors":"Yukun Jiang,&nbsp;Junhong Liang,&nbsp;Qingsong He","doi":"10.1007/s12061-026-09946-5","DOIUrl":"10.1007/s12061-026-09946-5","url":null,"abstract":"<div><p>Accurately quantifying urban renewal potential (URP) and understanding urban landscape influences on URP are critical for identifying renewal areas and improving intervention effectiveness. However, existing studies still lack a systematic and intelligent URP assessment framework, and the underlying relationships between landscape elements and URP remain insufficiently understood. Therefore, this study proposes a six-dimensional URP assessment framework that integrates large language models (LLMs) and the Genetic Algorithm–Particle Swarm Optimisation (GAPSO) algorithm for intelligent measurement, and applies interpretable machine learning to uncover nonlinear influences and threshold effects. Results show that high-potential blocks in Wuhan concentrate in waterfront areas of the urban core, with URP peaking around 2 km from the city centre. Overall, mean building area, building expandability, and public transportation convenience are the three most influential elements, contributing 23.77%, 11.21%, and 5.37%, respectively. All three exhibit U-shaped relationships with URP, reversing from negative to positive beyond 102.3, 3.9, and 31.2, respectively. The study further identifies strong bivariate interaction effects among the landscape elements. For example, low values of mean building area and building expandability interact negatively, whereas high values of public transportation convenience and road intersection quantities show positive synergy. Moreover, landscape elements’ influence mechanisms exhibit marked typological heterogeneity across high-potential block types (comprehensive enhancement, high economic return, and perception enhancement), with divergent dominant factors, shifting thresholds, and varying interactions. By applying artificial intelligence to complex renewal scenarios, this study provides a replicable, data-driven framework and empirical evidence for planners to identify street-block renewal priorities and implement precision-targeted interventions.</p></div>","PeriodicalId":46392,"journal":{"name":"Applied Spatial Analysis and Policy","volume":"19 3","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148613885","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 Impact of the Information Society on Sustainable Socio-economic Development Goals in European Regions: A Spatial and Hierarchical Regression Analysis","authors":"Ángel Luis Lucendo-Monedero,&nbsp;Francisca Ruiz-Rodríguez,&nbsp;Javier López-Otero","doi":"10.1007/s12061-026-09948-3","DOIUrl":"10.1007/s12061-026-09948-3","url":null,"abstract":"<div><p>Recent studies suggest that the effects of Information and Communication Technologies in households and by individuals (ICTs + Hi) on socio-economic sustainability in regions may vary both spatially (spatial dependence) and according to specific geographic scales (hierarchical dependence). The study´s objective is to analyse the impact of ICTs + Hi on the achievement of Sustainable Development Goals with a socio-economic dimension (SDGs-e) in European regions, using a hierarchical and spatial approach. Its analysis the influence of 9 ICTs + Hi variables (independent) on 3 SDGs-e´s factors (dependent) across 235 European regions grouped into 9 clusters according to the level of achievement in SDGs-e in 2021. The methodology compares a multilevel mixed linear regression model with a spatial regression model using a spatial weights matrix defined by “Block Weights”. The findings demonstrate that SEM model with “Block Weights” yields an appropriate framework for the explanation and prediction of regional variability in the impact of ICTs on SDGs-e. Results show that basic ICTs + Hi variables (e.g., internet access outside the home or SM participation) exert a greater impact on regions with lower levels of SDGs-e (SE Europe); whereas a higher use of advanced ICTs + Hi services (e.g., e-banking or interaction with public authorities) correlates with greater regional achievements of SDGs-e (Northern and Central Europe).</p></div>","PeriodicalId":46392,"journal":{"name":"Applied Spatial Analysis and Policy","volume":"19 3","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12061-026-09948-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148613955","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}