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Identifying optimal locations for sustainable commercial street development is crucial for driving economic growth and enhancing social vitality in cities. This study proposes a data-driven approach to predict potential sites for commercial streets in Foshan City, China, utilizing Points of Interest (POI) big data and machine learning techniques. Decision tree algorithms are employed to quantitatively assess and predict optimal locations at a fine-grained spatial resolution, dividing the study area into 9808 grid cells. The analysis identifies 2157 grid cells as potential sites for commercial street development, highlighting the significant influence of Medical Care, Shopping, and Recreation and Entertainment POIs on site selection. The study underscores the importance of considering population base, human activity patterns, and cultural elements in sustainable urban development. The main contributions include providing a novel decision-support method for data-driven and sustainable commercial street site selection and offering insights into the complex interplay between urban land use, human activities, and commercial development. The findings have important implications for urban planning and policy-making, showcasing the potential of data-driven approaches in guiding sustainable urban development and fostering vibrant commercial areas. [ABSTRACT FROM AUTHOR]

Human settlement areas significantly impact the environment, leading to changes in both natural and built environments. Comprehensive information on human settlements, particularly in urban areas, is crucial for effective sustainable development planning. However, urban land use investigations are often limited to two-dimensional building footprint maps, neglecting the three-dimensional aspect of building structures. This paper addresses this issue to contribute to Sustainable Development Goal 11, which focuses on making human settlements inclusive, safe, and sustainable. In this study, Sentinel-1 data are used as the primary source to estimate building heights. One challenge addressed is the issue of multiple backscattering in Sentinel-1's signal, particularly in densely populated areas with high-rise buildings. To mitigate this, firstly, Sentinel-1 data from different directions, orbit paths, and polarizations are utilized. Combining ascending and descending orbits significantly improves estimation accuracy, and incorporating a higher number of paths provides additional information. However, Sentinel-1 data alone are not sufficiently rich at a global scale across different orbits and polarizations. Secondly, to enhance the accuracy further, Sentinel-1 data are corrected using nighttime light data as additional information, which shows promising results in addressing multiple backscattering issues. Finally, a deep learning model is trained to generate building height maps using these features, achieving a mean absolute error of around 2 m and a mean square error of approximately 13. The generalizability of this method is demonstrated in several cities with diverse built-up structures, including London, Berlin, and others. Finally, a building height map of Iran is generated and evaluated against surveyed buildings, showcasing its large-scale mapping capability. [ABSTRACT FROM AUTHOR]

In the context of rapid urbanization, urban population differentiation has become increasingly pronounced. Regional development strategies based on growth scenarios often lead to continuous expansion, regardless of urban population status. Such "one-size-fits-all" models exacerbate resource waste and negatively impact urban land use efficiency (ULUE). This study aims to explore the mechanisms by which urban population growth and shrinkage (UPGS) affect ULUE, with the goal of enhancing ULUE and promoting sustainable urban development. We analyzed 34 prefecture-level cities in China's three northeastern provinces. First, we identified UPGS using population data. We then employed a three-stage SBM-DEA model to measure ULUE from 2000 to 2020. Spatial analysis methods were used to examine the spatiotemporal characteristics and correlations between UPGS and ULUE. Additionally, mediating effect models and spatial Durbin models were utilized to empirically test the impact processes, mechanisms, and spatial heterogeneity. Our findings reveal that: (1) Over the past 20 years, urban population shrinkage in northeastern China has intensified, and significant regional disparities in urban development are evident. (2) Population growth positively influences ULUE, while population shrinkage inhibits its improvement. (3) Economic development, technological innovation, and industrial structure upgrading are key factors in enhancing ULUE in this region, while the impact of public services on ULUE varies significantly at different stages of urban development. (4) Economic development, technological innovation, and industrial structure upgrading exhibit spatial spillover effects, whereas public services are constrained by regional limitations, resulting in minimal spatial spillover effects. To foster coordinated regional development, this study proposes policy recommendations, including strengthening support for resource-dependent cities, optimizing the allocation of public resources, and promoting technological innovation and industrial diversification. [ABSTRACT FROM AUTHOR]

The rapid increase in population and economic activities has greatly influenced land use and spatial development. In urban agglomerations where socioeconomic activities are densely concentrated, the clash between ecological protection and economic growth is becoming more evident. Therefore, a thorough quantitative assessment of spatial changes driven by land use dynamics, alongside an examination of temporal and spatial driving factors, is crucial in offering scientific backing for the long-term and sustainable growth of urban agglomerations. This paper focuses on the major urban agglomerations in China's Yangtze River Delta region, examining the spatiotemporal evolution of land use and landscape patterns from 2000 to 2020. By employing the standard deviation ellipse technique, coupled with multiple linear regression and the geographical detector model, we conduct a quantitative assessment of the directional trends in urban construction land expansion as well as the diverse impacts of temporal and spatial factors on this expansion across various periods and regions. The findings indicate that over the past 20 years, construction land in the Yangtze River Delta Urban Agglomeration expanded in concentrated patches, showing significant scale effects with relatively intact farmland and forest land being increasingly encroached upon. Landscape-type transitions predominantly occurred in cities around Taihu Lake and Hangzhou Bay, with the most significant transition being farmland converted to construction land, resulting in a greater number of patches and more pronounced land fragmentation. Throughout the 20 years, the standard deviation ellipse of construction land in the Yangtze River Delta Urban Agglomeration expanded and shifted, with the predominant expansion trending from the northwest toward the southeast, and the EN orientation being the most intense expansion area, covering 1641.24 km2. The influence of temporal and spatial driving factors on the expansion of urban construction land differed across various periods and regions. This study thoroughly examines the driving factors that affect the evolution of urban construction land in the region, offering valuable scientific evidence and references for future planning and development of the Yangtze River Delta Urban Agglomeration, aiding in the formulation of more precise and efficient urban management and land use strategies. [ABSTRACT FROM AUTHOR]

Optimizing urban land use is of significant practical importance for promoting economic development, enhancing the standard of living for individuals residing in metropolitan areas, enhancing urban infrastructure and public services, driving urban transformation and upgrading, and attaining synchronized progress of the economy, society, and environment. This paper uses the super-efficiency SBM model to measure the urban land use efficiency (ULUE) of 13 cities in the Beijing–Tianjin–Hebei (BTH) urban agglomeration from 2005 to 2020 and explores the spatiotemporal evolution characteristics and influencing factors of ULUE in this urban agglomeration using analysis of spatial data and application of geographic detector methods. The results show that (1) from 2005 to 2020, the ULUE of the BTH urban agglomeration had an initial rise followed by a decline; however, the overall efficiency score is above 1, suggesting an overall effective state; (2) a distribution pattern with Beijing as its core was established, exhibiting greater ULUE in the northern region and poorer efficiency in the southern region, with significant correlation characteristics in efficiency values between adjacent cities; and (3) capital input, labor input, social welfare, and ecological environment are all influencing factors that promote the improvement in ULUE in the BTH region, and the interaction of any two factors explains the ULUE in this region better than a single factor. The empirical research results can provide useful references for improving the input–output ratio of land units and further spatial planning and policy formulation in the BTH region. [ABSTRACT FROM AUTHOR]

In recent years, China's urbanization has accelerated, significantly impacting ecosystems and the carbon balance due to changes in urban land use. The spatial patterns of CO2 emissions from urban land are essential for devising strategies to mitigate emissions, particularly in predicting future spatial distributions that guide urban development. Based on socioeconomic grid data, such as nighttime lights and the population, this study proposes a spatial prediction method for CO2 emissions from urban land using a Long Short-Term Memory (LSTM) model with added fully connected layers. Additionally, the geographical detector method was applied to identify the factors driving the increase in CO2 emissions due to urban land expansion. The results show that socioeconomic grid data can effectively predict the spatial distribution of CO2 emissions. In the Yangtze River Economic Belt (YREB), emissions from urban land are projected to rise by 116.23% from 2020 to 2030. The analysis of driving factors indicates that economic development and population density significantly influence the increase in CO2 emissions due to urban land expansion. In downstream cities, CO2 emissions are influenced by both population density and economic development, whereas in midstream and upstream city clusters, they are primarily driven by economic development. Furthermore, technology investment can mitigate CO2 emissions from upstream city clusters. In conclusion, this study provides a scientific basis for developing CO2 mitigation strategies for urban land within the YREB. [ABSTRACT FROM AUTHOR]

Exploring the regional differences and convergence of urban land green use efficiency (ULGUE) is of great significance for urban green development and the goal of carbon neutrality. Based on the panel data of 282 prefecture-level cities in China from 2006 to 2019, this paper uses the SBM-Undesirable model to calculate ULGUE in China, then uses Dagum Gini coefficient decomposition, coefficient of variation and panel data model to reveal the regional differences and convergence of ULGUE. The results show that ULGUE in China increased from 0.441 to 0.555 in 2006–2019. The number of cities in the medium–low efficiency range is dominant, accounting for more than 54%. There are differences in ULGUE among the three major regions, which shows the distribution pattern of west > east > central. ULGUE in China has significant spatial agglomeration characteristics, and its overall, intra-regional and inter-regional differences show a downward trend. Over-variable density is the main source of the overall difference, with an average contribution rate of 49.71%. ULGUE across the country and in the three regions of east, central and west in China exist significant σ convergence, absolute β and conditions β convergence. The research results are helpful to objectively evaluate ULGUE in China, and provide decision-making reference for promoting the green, low-carbon and sustainable use of urban land and achieving the goal of carbon neutrality. [ABSTRACT FROM AUTHOR]

Intensive urban land use is a strategy to enhance productivity and mitigate environmental challenges in emerging regions, but its relationship with carbon emissions needs further city-level investigation. This study investigates the impact of intensive urban land use on carbon emissions across 153 cities in China, thus employing the STIRPAT model with the ordinary least square (OLS) and geographical weighted regression (GWR) methods. The findings underscore the heterogenous influence of intensive urban land use on carbon emissions across China's urban landscapes: (1) R&D investment intensity and population density show significant negative association with carbon emissions in general. (2) Capital investment intensity positively affects carbon emissions in low-income cities, R&D investment intensity shows negative effects on carbon emissions in middle-income cities, and population density emerges as a substantial factor in reducing carbon emissions in both middle- and low-income cities. (3) Capital intensity, labor intensity, and R&D investment intensity exert positive effects on emissions in middle China and negative influences in northeastern and southern China, whereas population density shows converse spatial effects. Based on the study's results, tailored policy implications are provided for urban planning authorities in emerging regions. [ABSTRACT FROM AUTHOR]

Unplanned urbanization continues to threaten the sustainability of cities in the global south. This study seeks to assess the urban planning response to the effects of sprawl on the urban form and land use pattern of Kumasi. The study relied on geospatial techniques to determine changes in land use patterns, landforms, and sprawl. A qualitative research approach was used to examine urban planning response to the causes and effects of urban sprawl in Kumasi. The results of this study show that the higher the urbanization rate, the more the built-up area increases, causing a significant loss of natural assets such as farmlands. In terms of shape, the research showed that the Greater Kumasi Metropolis has shifted from the historically monocentric city structure to a polycentric city. The city currently has multiple centers or subcentres for commercial, cultural, and social activities. In spite of all the threats posed by urban sprawl on the metropolis, there no clear policy intervention put in place to address the adverse effects of sprawl in the Greater Kumasi Metropolis. The researchers recommend that city authorities should adopt the compact city approach to control the high rate of sprawl to promote densification. [ABSTRACT FROM AUTHOR]

High spatio-temporal resolution estimates of electricity consumption are essential for formulating effective energy transition strategies. However, the data availability is limited by complex spatio-temporal heterogeneity and insufficient multi-source feature fusion. To address these issues, this study introduces an innovative downscaling method that combines multi-source data with machine learning and spatial interpolation techniques. The method's accuracy showed significant improvements, with determination coefficients (R2) increasing by 30.1% and 33.4% over the baseline model in two evaluation datasets. With this advanced model, we estimated monthly electricity consumption across 1 km x 1 km grid for 280 Chinese cities from 2012 to 2019. Our dataset is highly consistent with officially released electricity consumption of different industries (Pearson correlation coefficients within 0.83 - 0.91). Moreover, our data can reflect the electricity consumption patterns of different urban land uses compared to other datasets. This study bridges a significant gap in fine-grained electricity consumption data, providing a robust foundation for the development of sustainable energy policies. [ABSTRACT FROM AUTHOR]

Urban land consumption negatively impacts ecosystem services such as food production, carbon regulation, and water absorption. Despite indications of densification in some large cities, evidence shows that built-up land has been increasing faster than population globally. However, existing research disproportionally focuses on large cities, overlooking intermediate and rural territories. Our study examines the trajectories of urban land consumption along the urban-rural gradient in France between 1975 and 2020. We quantify built-up land per capita (BPC) and the changes therein and compare it to a 7-level classification of territories according to their density. BPC increased 59%, combining a 95% increase in built-up land and a 23% population growth. Yet, BPC change varied considerably across the seven territory classes: very scattered rural areas experienced the largest growth (+190 m2) while large cities grew more efficiently (+11 m2). Intermediate territories, such as peri-urban areas and small cities, increased BPC by 65% and 78%, respectively. Our findings confirm the hypothesis of urban land use polarization, as rural areas were sparser than large cities in the base year, and this difference increased over time. This underlines the urgency to address the distinct land use trajectories for effective no net land take policies. [ABSTRACT FROM AUTHOR]

Land use can affect energy consumption by changing the economic and social structure of cities. Thus, the optimization of land use patterns is key to promoting energy sustainability. In this study, we explored the spatiotemporal evolution of China's urban energy consumption and its driving factors from the role of land use, with the application of high-precision nighttime light images and land use data acquired from remote sensing satellites. A deep clustering network in deep learning was used for clustering analysis of urban energy consumption. The results indicated that the economic and structural effects of land use were the primary driving factors of the increasing urban energy consumption, whereas the decrease in the energy intensity (caused by technological progress) restrained the growth rate of energy consumption. With the exception of economically developed cities, generally, the contribution of the population size to the temporal increase in energy consumption was relatively small. The spatial difference in urban energy consumption was mainly due to the between-group differences among the diversified cluster groups, which were strongly influenced by land urbanization and population size. These conclusions can help the Chinese government formulate differentiated urban energy policies. [ABSTRACT FROM AUTHOR]

The Western Taiwan Strait (WTS) Economic Zone connects the Yangtze River Delta and the Pearl River Delta, playing a significant role in China's coastal economy and forming part of the East Asian economic structure. This study used panel data from 20 cities in the WTS Economic Zone, spanning 2011 to 2020, to investigate urban land use efficiency and its dynamic evolution characteristics. The study used a super-efficiency EBM model, which accounts for undesirable outputs, combined with kernel density estimation and Malmquist–Luenberger (ML) index analysis, to thoroughly examine the changes in total factor productivity (TFP) of urban land use and the factors driving these changes within the WTS Economic Zone. The findings are as follows: (1) From 2011 to 2020, the overall trend of urban land use efficiency in the WTS Economic Zone was upward, with coastal areas generally exhibiting higher urban land use efficiency compared to inland areas. (2) The urban land use efficiency of cities in the WTS Economic Zone displayed four types of changes: rising, stable, "U"-shaped, and inverted "U"-shaped. (3) The TEP index of the WTS Economic Zone exhibited a right-leaning "M" trend. Technological change was the primary driver of enhanced urban land use efficiency, although there is still room for improvement in technical efficiency. Based on these findings, this study proposes policy insights to foster high-quality development of urban land use efficiency in the WTS Economic Zone. [ABSTRACT FROM AUTHOR]

Given the challenges of restricted land resources and net-zero carbon city initiatives, it is crucial to seek a balance between intensive land use and carbon neutrality during the construction of development zones. By incorporating net-zero carbon balance into the land use efficiency evaluation system and utilizing a quasi-natural experiment based on the 2009 provincial development zone upgrading policy, this study investigates the policy's impact on urban land use efficiency under the net-zero carbon city goal. The study finds that the upgrading of provincial development zones significantly enhances urban land use efficiency, while exhibiting the dual effects of reducing carbon emissions and increasing carbon sinks. Mechanism analysis reveals that the upgrade policy improves land use efficiency by enhancing land-use and environmental regulations, optimizing resource allocation, and fostering green technological innovation. Heterogeneity analyses show that the policy effect is more significant in eastern and central cities, with the impact being strongest in central cities. Additionally, the impact of upgrading to a national high-tech development zone is greater than that of upgrading to a national economic development zone. This article provides insights into how to use industrial policies effectively to achieve intensive land use and high-quality development while aiming for carbon neutrality. [ABSTRACT FROM AUTHOR]

Land intensive use reflects the spatial structure, agglomeration characteristics, and internal mechanisms of urban economic, social, and ecological system development, significantly impacting urban resilience. Based on panel data from 287 cities in China from 2010 to 2020, this paper measures the levels of land intensive use and urban resilience, and empirically examines the impact mechanism of land intensive use on urban resilience through baseline regression and panel quantile regression. The results reveal that: (1) During the study period, China's urban land intensive use level has significantly improved. The land intensive use level shows a trend of "the strong become stronger, and the weak are always weak" and "high in the east and low in the west" spatial differentiation, while the urban resilience level showed a trend of accelerated "catching up" of low-resilience cities towards high-resilience cities and "high in the east and low in the west" spatial differentiation as well. (2) Land intensive use significantly promotes effect on urban resilience, and the effect depends on different conditions. (3) Among all dimensions of land intensive use, both land input intensity and land use benefits significantly promote urban resilience, while land use intensity shows an insignificant effect. (4) The impact of land intensive use on urban resilience demonstrates significant scale heterogeneity and geographic regional heterogeneity. Based on these findings, the paper proposes relevant policy suggestions for land intensive use aimed at improving urban resilience, offering guidance for promoting high-quality land use and sustainable urban resilience development. [ABSTRACT FROM AUTHOR]

Rapid morphological and socioeconomic changes have accelerated the urbanization process and urban land use transformation in China. Megacities comprise clusters of urban cities and exhibit both newly formed and well-developed urban land use development beyond administrative boundaries. It is necessary to distinguish the changing effects of spatial-varying driving factors on newly formed urban land uses from well-developed built-up areas in megacities. This study proposed a multi-spatial urbanization framework to quantify region-level socioeconomics, cluster-level ecological morphologies, and grid-level urban functional morphologies. A three-level Bayesian hierarchical model was developed to investigate the impacts of multi-spatial driving factors on urban land use transformation in megacities. The study period focused on the urbanization process between 2000 and 2018 in Guangdong–Hong Kong–Macao Greater Bay Area (GBA). Results revealed that compared with well-developed urban built-up land, changing impacts of three-level driving factors in urban land use transformation could be captured based on the proposed Bayesian hierarchical model. The region-level total population was associated with increasing possibilities in forming new residential land than the well-developed ones in 35 districts/counties/cities in GBA. Cluster-level ecological attributes with higher proportion, lower edge density of urban built areas, and lower-degree ecological complexity showed increasing probability on newly formed industrial and public land. Grid-level urban functional factors including public transportation density and shopping/dining distribution exhibited significantly decreasing probability (coefficients: −2.12 to −0.51) on contributing newly formed land uses compared with the well-developed areas, whereas business/industry distribution represented higher (coefficients: 0.99 and 0.15) and lower probabilities (coefficient: −0.22) of forming industrial/public land and residential land separately. This research shows a new attempt to distinguish multi-spatial morphological and socioeconomic effects in urban land use transformation in megacities. [ABSTRACT FROM AUTHOR]

Improving urban land use efficiency and greenness is essential for achieving sustainable urban development and improving the well‐being of urban settlements. Using satellite‐based land use data, socioeconomic data, and sustainable development goal (SDG) indicators, we quantified the changes in the urban land use efficiency and greenness under rapid urban expansion in provincial cities in the Yangtze River Economic Belt (YREB) from 1990 to 2020. Over this period, urban area increased significantly, by 254.57%, at a rate of 191.41 km2 per year in the YREB, as national macro‐policies shifted the center of urban expansion westward. Nevertheless, the urban land use efficiency and greenness have both increased. The ratio of land consumption rate to population growth rate increased from 0.55 in 1990–2000 to 0.83 in 2010–2020 in the YREB, accompanied by an increase in the investment output ratio of fixed assets. In addition, the population‐weighted greenspace exposure increased by 5.31%, due to an 8.16% increase in the urban greenspace coverage, which improved the urban living environment amidst substantial urban population growth. We emphasize the importance of fostering coordinated development between central and western cities in the YREB. The results provide an important reference for the rational allocation of land resources, improving land use efficiency, and building resilient cities within global urbanization hotspots under the SDG framework. [ABSTRACT FROM AUTHOR]

Urban green spaces provide several benefits related to the quality of urban life. The existence and spatial arrangement of these spaces within neighbourhoods and functional land uses have significant implications for the well-being of urban dwellers. Previous studies on green spaces in urban areas of Malawi have focused on a broader and macro-level perspective, offering insightful information on general trends in different cities. However, there is a significant research shortage in localised understanding, which requires carrying out micro-level assessments concentrating on land use zones and neighbourhoods within these cities. In this study, we used remote sensing data and landscape metrics to understand the distribution and configuration of urban green spaces in the city's neighbourhoods and functional land uses and their relationship with urban form. The study revealed that 20% of neighbourhoods fail to meet the WHO-recommended standard of 9 m2 of green space per person, with a predominant concentration of these undersupplied areas in high-density and quasi-residential zones. In addition, 56.2% of Lilongwe City's total green area was contained under functional land uses. Particularly, high-rise residential, medium-density residential, low-density residential, quasi-residential, high-rise flat area, commercial class, high-rise commercial, heavy industry, light industry, and government land use zones contained 17.3%, 12.0%, 22.2%, 12.0%, 4.1%, 6.4%, 6.1%, 5.0%, 1.6%, and 13.3% of the total green spaces in functional land uses, respectively. Importantly, this research found significant correlations between urban form metrics, namely building coverage, building density, building perimeter area ratio, road density, and the distribution and configuration of urban green spaces. This necessitates an integrated approach to urban planning and design, emphasising the importance of balancing development with green space preservation. [ABSTRACT FROM AUTHOR]

An expansion of impervious surfaces in urban areas leads to increases of nutrient loads discharged with the surface runoff to receivers. A study of a different density of urban development impact on total nitrogen (TN) and phosphorus (TP) loads from the city of Lublin (eastern Poland) with the use of the SWAT (Soil & Water Assessment Tool) model was performed. To distinguish between areas with high and low density of urban development (UHD and ULD), a special analysis of hydrological parameters has been proposed. Moreover, to investigate the impact of climate change, four variant scenarios were taken into account, combining the RCP (representative concentration pathway) 4.5 and 8.5 forecasts and the adopted time horizons (2026–2035 and 2046–2055). The results showed a much higher share of TN and TP from UHD compared to ULD (86%—32 022 kg/year and 89%—2574 kg/year, respectively). In addition, the variant scenarios showed that the forecasted increase in precipitation and temperature will result in increased loads of nutrients from UHD and ULD up to 30%. Furthermore, the current increase of inhabitant number, due to the Ukrainian war migration and the common tendency to convert agricultural land to residential areas, could contribute to further expansion of UHD and ULD areas and an additional increase of nutrient loads. [ABSTRACT FROM AUTHOR]

Urban land serves as the medium for urban economic and residential activity. The degree of intensive and efficient use of urban land and the coordinated growth of the economy and society are significantly impacted by utilization efficiency. Northwest China's temperature, geography, water, and soil characteristics make less of the region suited for urban development. Thus, it is imperative to investigate Northwest China's urban land use efficiency (ULUE). This study applies the efficiency loss model to highlight the discrepancy between the optimal and actual values of each input-output variable of the ULUE, and uses the Super-SBM model to compute the ULUE of 39 cities. Additionally, the internal driving elements of the ULUE are examined using the Global Malmquist-Luenberger (GML) index. This study discovered that Northwest China had a low ULUE, with a modest upward trend seen between 2004 and 2018. The overall shape of the region was similar to an 'N' curve, with high efficiency values concentrated in the southeast of the region in 2018. When considering the agglomeration impact of urban agglomeration, these results are dependable. Moreover, the efficiency loss model discovered that pollution and wasteful resource usage were the main reasons behind the drop in ULUE in Northwest China. Finally, the GML index and its constituents revealed that the main force behind ULUE's growth in Northwest China is technological developments. This research provides important policy implications for reducing the gap between eastern and western regions, supporting high-quality development in northwest China, and protecting and intensively using urban land. [ABSTRACT FROM AUTHOR]

Geospatial data has proven useful for monitoring urbanization and guiding sustainable development in rapidly urbanizing regions. The United Nations' (UN) Sustainable Development Goal (SDG) Indicator 11.3.1 leverages geospatial data to estimate rates of urban land and population change, providing insight on urban land use expansion patterns and thereby informing sustainable urbanization initiatives (i.e., SDG 11). Our work enhances a UN proposed delineation method by integrating various open-source datasets and tools (e.g., OpenStreetMap and openrouteservice) and advanced geospatial analysis techniques to automate the delineation of individual functional urban agglomerations across a country and, subsequently, calculate SDG Indicator 11.3.1 and related metrics for each. We applied our automated geospatial approach to three rapidly urbanizing countries in Africa: Ethiopia, Nigeria, and South Africa, to conduct multi-level examinations of urban land use expansion, including identifying hotspots of SDG Indicator 11.3.1 where the percentage growth of urban land was greater than that of the urban population. The urban agglomerations of Ethiopia, Nigeria, and South Africa displayed a 73%, 14%, and 5% increase in developed land area from 2016 to 2020, respectively, with new urban development being of an outward type in Ethiopia and an infill type in Nigeria and South Africa. On average, Ethiopia's urban agglomerations displayed the highest SDG Indicator 11.3.1 values across urban agglomerations, followed by those of South Africa and Nigeria, and secondary cities of interest coinciding as SDG Indicator 11.3.1 hotspots included Mekelle, Ethiopia; Benin City, Nigeria; and Polokwane, South Africa. The work presented in this study contributes to knowledge of urban land use expansion patterns in Ethiopia, Nigeria, and South Africa, and our approach demonstrates effectiveness for multi-level evaluations of urban land expansion according to SDG Indicator 11.3.1 across urbanizing countries. [ABSTRACT FROM AUTHOR]

Based on the panel data of Guangxi from 2005 to 2017, the spatiotemporal characteristics and determinants of urban carbon emissions in Guangxi were analyzed using the extended STIRPAT model and the Geographically and Temporally Weighted Regression (GTWR) model. The main findings of our research can be summarized as follows. While the total carbon emissions of cities in Guangxi consistently increased from 2005 to 2014, the growth trend slowed after 2014, leading to a stabilization in the total emissions. In addition, there are significant differences in the total carbon emissions among the cities. The central and northeastern regions have higher emissions, while the southwestern region has lower emissions. Finally, there are variations in the degrees and directions of the impacts that factors have on carbon emissions among the different time periods and cities. Urban land use is a key factor driving carbon emissions, and it has a negative impact on most cities in Guangxi. Meanwhile, factors such as industrial structure, population urbanization, population concentration, and economic growth have significant positive effects on carbon emissions in Guangxi. The influence of urban roads on carbon emissions is generally positive, while the degree of openness to the outside world and environmental regulations has relatively weaker impacts on emissions. In summary, in order to promote the low-carbon transition of Guangxi and achieve high-quality development, the cities in Guangxi should implement differentiated urban carbon reduction strategies that are focused on optimizing urban land use and industrial structure. [ABSTRACT FROM AUTHOR]

Although the relationship between the size of urban industrial land use and pollutant emissions has been widely discussed from different perspectives (e.g., the scale and crowding effects), the results of various studies have revealed positive, negative, and combined impact relationships. However, how the expansion of urban industrial land use affects SO2 emissions remains unknown. We need to clarify this relationship in order to facilitate the realization of China's pollution reduction and emission reduction goals. This study used the panel data from 294 cities spanning from 2011 to 2019 to construct a spatial econometric model. The objective was to explore the correlation between the scale of urban industrial land and sulfur dioxide emissions in China. The results show that a large scale of urban industrial land use corresponds to lower sulfur dioxide emissions per unit of industrial added value. By gaining a deeper understanding of the relationship between the scale of urban industrial land use and sulfur dioxide emissions, policymakers can further reduce pollutant emissions by rationalizing the planning of urban industrial land use and industrial layout. In addition to promoting industrial agglomeration and economies of scale in cities with extensive industrial land use, this strategy can support the development of efficient and environmentally friendly industries in areas with limited industrial land use. Optimizing the technology and encouraging the development of green industries can help reduce environmental pollution and promote sustainable urban development. [ABSTRACT FROM AUTHOR]

High-quality utilization of urban land (HUUL) is essential for optimizing urban land use and promoting high-quality development. Previous research has mainly focused on examining urban land use efficiency, neglecting the connection between urban land use and high-quality development. This study reveals the intrinsic association mechanism between high-quality development and urban land use, which can provide comprehensive theoretical and empirical support for high-quality land use and high-quality urban development. This study constructed an evaluation system for HUUL that comprehensively adopted the entropy method, kernel density estimation, and the geodetector model to analyze the spatio-temporal evolution and driving factors of the HUUL levels of 284 Chinese cities from 2006 to 2020. The measurement results showed that during 2006–2020, the HUUL level showed an apparent upward trend, and the eastern region > the central region > the overall region > the western region > the northeast region. From the kernel density map, there was a noticeable trend of varying degrees of increase in the difference of the main peak position of the HUUL level among cities in all regions except the west. Furthermore, some cities in the eastern and western regions had significantly higher HUUL levels than the others. According to the results of the factor analysis, it is evident that innovative use and open use are the internal primary factors that drive the enhancement of the HUUL level. Moreover, the level of economic development is the external primary factor that facilitates the improvement in HUUL level. [ABSTRACT FROM AUTHOR]

Urban land is the primary location for manufacturing and services, facilitating the expansion and interconnectedness of economic activities and factor flows to shape various urban land-use performances (ULUP). Exploring these spatial linkages of urban land-use performance can play a crucial role in fostering cohesive urban development. Taking 109 prefecture-level cities as research samples, this paper explores the characteristics and influencing factors of the spatial network associated with ULUP in the Yangtze River Economic Belt through modified gravity model, network analysis and QAP analysis. The analysis shows that ULUP has shown an N-shaped trend over the past two decades. It has formed a network of correlations, with Chongqing, Shanghai, and Wuhan emerging as central nodes. Notably, the correlation predominantly occurs between geographically adjacent cities, with weaker links between the Yangtze River Economic Belt's upstream, midstream, and downstream regions. The network can be divided into four distinct types: main inflow plate, main outflow plate, agent plate, and bidirectional spillover plate. Geographic location is the most significant factor influencing network formation, followed by resource mismatch, economic development, and openness. The study provides theoretical guidance and empirical support for improving the utilisation of urban land and promoting coordinated development. [ABSTRACT FROM AUTHOR]

Over the past few decades, China has undergone the largest and fastest urbanization process in world history. By 2023, Chengdu's urbanization rate had reached 80.5%, significantly higher than the national average of 66.16%. Studying the urbanization experience of Chengdu is of great significance for optimizing urban planning policies in Chengdu and other cities in China. Although much literature has explored the urbanization process from macro and micro perspectives, studies using a top-down approach to examine urban fringe expansion are relatively scarce. This study first applies the entropy weight method to analyze the spatial-temporal evolution trends of urban development, identifying areas of imbalanced development and prominent issues. Secondly, the K-means machine learning algorithm and nightlight data are used to reconstruct and classify urban regions, and a comparative analysis is conducted with administrative divisions to further identify unreasonable areas in urban spatial distribution and structure. Finally, POI data and the geographical detector method are used to analyze the micro-driving forces in areas of imbalanced development, identifying major limiting factors and solutions. The study found that the gap between urban and rural development in Chengdu is narrowing during the urbanization process, but there is severe differentiation in the second circle of Chengdu, where economic development is accelerating but residents' happiness is declining. Moreover, analysis based on urban nightlight data and land-use data reveals that the expansion areas on the urban-rural fringe are mainly concentrated in the second circle of Chengdu. Micro-level driving factor analysis found that the western region of the second circle has many but small urban settlements, with a dense road network but scattered functional areas. The eastern region has inefficient and extensive use of construction land. Additionally, the mismatch between student status and household registration has resulted in relatively lagging educational resource development, and high entry barriers have hindered the progress of urbanization, leading to low per capita welfare expenditure. These reasons are the main factors causing the decline in residents' happiness, and this impact shows significant differences at different temporal and spatial scales. Encouraging innovation in research and development or education can serve as a long-term and effective driving force for promoting sustainable urbanization. This study provides valuable insights for scientifically planning sustainable urban development and promoting the urbanization process. [ABSTRACT FROM AUTHOR]

The establishment of the Free Trade (Pilot) Zone (FTZ) is a new attempt by China to embark on modern sustainable urban planning and governmental territorial management at the development stage of a high-quality economy. As urban lands serve as the limited resource foundation for civic production and livelihoods, enhancing land use efficiency becomes a key measure to facilitate metropolitan economies of high quality. Utilizing panel data from prefecture-level cities spanning from 2005 to 2021, this study constructs a multi-period difference-in-differences model to explore the impact of FTZs on urban land use efficiency (ULUE). The results indicate that FTZs can significantly enhance urban land use efficiency, with a more pronounced policy effect observed in central cities, inland cities, and cities with higher urbanization rates. Furthermore, the FTZs also demonstrate spatial spillover effects on urban land use efficiency. This study holds great significance for relevant government departments in formulating policies to optimize land resource allocation, promote FTZ development, and foster high-quality urban planning and territorial management. [ABSTRACT FROM AUTHOR]

The advancement of the big data industry is playing a pivotal role in urban land management refinement. Recently, China initiated a big data strategy, establishing national big data comprehensive pilot zones (NBDCPZs) across diverse regions. These initiatives present substantial opportunities for enhancing the urban land green use efficiency (ULGUE). Consequently, in this study, we utilized the super-efficiency slack-based measure (SBM) model with undesirable outputs to assess the ULGUEs across 281 prefecture-level cities in China from 2006 to 2021. Subsequently, leveraging the NBDCPZ establishment as a quasi-natural experiment, we employed the difference-in-differences (DID) method to empirically explore the impact of the NBDCPZ policy on the ULGUE for the first time. The findings revealed the following: (1) The implementation of the NBDCPZ policy significantly enhances the ULGUE; (2) the effects are mediated through mechanisms such as fostering technological innovation, mitigating resource misallocation, and promoting industrial agglomeration; (3) the heterogeneity analysis emphasizes the increased policy effectiveness in cities characterized by fewer natural resources, lower economic growth pressures, stable development stages, and moderate digital infrastructure and human capital levels; and (4) further analysis demonstrates the significant positive spillover effects of the NBDCPZ policy on the ULGUEs of neighboring non-pilot cities, with a diminishing impact as the proximity between pilot and non-pilot cities decreases. Overall, this study contributes to the literature on the relationship between the digital economy and land utilization, offering valuable insights for achieving sustainable urban development. [ABSTRACT FROM AUTHOR]

The urban radio‐frequency (RF) noise generated by our cities continues to change with time. Although models exist to describe the RF noise as functions of frequency and urban land use types, very few models describe the spatial character or structure of the noise on the scales of city blocks (50–150 m). The goal of this work is to investigate the connection between urban morphology and the higher‐order spatial statistics of the noise field. To achieve this goal, a large measurement campaign was conducted in Boston, Massachusetts. Many spatial measurements allowed for calculation of spatial correlation functions of noise power in three different neighborhoods, which were used to quantify the spatial structure of the fields. A statistical point source model is then developed, with adjustable parameters relating to urban morphology. Good agreement between the model and the experimental correlation functions suggests the 25 MHz urban noise field is well described by a random network of fixed point sources, radiating with a 1/r power law behavior. Plain Language Summary: Our modern cities are filled with electronic devices. Each device can emit radiation and contribute to what is called the urban radio‐frequency noise field. The noise field is the combined effect from all these devices. If the noise field is strong enough it can negatively impact wireless communication, and the use of other electric devices. It is important to better understand the nature of the noise field in order to mitigate and plan for its negative effects. This paper describes in detail how the noise field is distributed in space, or its spatial structure. A theoretical model or tool is developed to help predict how the noise field looks spatially. Key Points: Radio‐frequency noise spatial correlation functions were calculated for three different neighborhoods in Boston, MassachusettsGood theoretical agreement suggests the noise field is well described by a random network of point sources radiating with a 1/r behavior [ABSTRACT FROM AUTHOR]

Although cities cover 2% of the earth's surface, their impact on climate change and vulnerability to climate change-related hazards and risks are high. The effects of climate change vary depending on geographical areas and characteristics of living spaces around the world. In this regard, the effects of climate change are clearly seen in urban areas, which are classified as cold climate cities. Since the cities are the places which both have impacts on and are affected by climate change it is necessary to conduct scientific studies which can serve as a basis for planning, design and application practices to both mitigate the effects of the urban factors causing climate change and adapt to the effects of climate change. The aim of this study is to provide literature-based information about the climate hazards and risks, which become evident in cold climate cities, and to reveal the principles of urban design approaches to mitigate and adapt to the effects of climate change in cities. As the result of the study, it was revealed that mitigation and adaptation to climate hazards and risks can be possible through spatial planning and design in cold climate cities. Factors coming forth and the topics evaluated in terms of climate adaptive urban planning and design in cold climate cities can be classified as follows, efficient benefiting from sunshine duration and solar radiation, use of wind effect to mitigate the effects of air pollution and urban heat islands, management of rain and snow storms, adaptation of the design of urban land use and structures, choosing suitable urban surface covering materials, sustainability of urban vegetation, revising urban transportation systems. [ABSTRACT FROM AUTHOR]

It is proven a powerful tool to reduce carbon emissions by regulating urban land use structure. To leverage this tool, land-use carbon emission intensity (LUCEI) is the critical indicator to bridge carbon emissions effects of natural and human activities to land use structure. Current studies try to established the carbon emission allocation framework of "carbon emission inventory - land use category" and use it to estimate the LUCEI. However, none of the frameworks work at multiple spatial scales: the total carbon emissions and the land use category at different territorial levels do not conform with each other, impeding the synergetic effects of the low-carbon planning at different territorial levels. This study establishes a new framework of carbon emission estimation and allocation for the three territorial levels of municipality, city proper, and district and conducts a case study on Hinggan Prefecture. With the new framework, the carbon emission intensity of the entire land use category is being systematically differentiated, enabling the clarification of the linkages between LUCEI and the characteristics of energy structure, industrial structure, and urban forms of cities. The comparison of LUCEI of residential and transportation land between cities identifies the characteristics of urban form that are apt to high carbon emissions. The biggest contribution of the framework established in this study provides a technical tool to introduce carbon emission quantification into the territorial and spatial planning system. It may help deepen the understanding about the spatial pattern of carbon emissions and support a more refined estimation and comparison of the carbon effects of land use planning schemes. [ABSTRACT FROM AUTHOR]

To comprehend environmental changes and develop sustainable land management plans, it is crucial to thoroughly understand the complexities involved in land use change. This study investigated land use changes in Mae Sot district, Tak province, Thailand, in the years 2003, 2013, and 2023 using pixel-based supervised classification employing the Random Forest (RF) algorithm. The findings revealed that a substantial portion of the area is dedicated to agricultural purposes, accompanied by a decrease in forest areas and an increase in urban development. Furthermore, five spatial factors—elevation, slope, distance from the city center, distance from the main road, and population—were integrated as explanatory variables in training the cellular automata-artificial neuron network (CA-ANN) model to construct a land use change simulation for 2023. A comparison of the projected and actual land use maps for 2023 demonstrated a high level of agreement, with a kappa coefficient of 0.81, confirming the reliability of the model’s predictions. Subsequently, the CA-ANN model was used to forecast future land use for the year 2033. The projection suggested a significant expansion of built-up areas, primarily originating from the core of Mae Sot sub-district and spreading towards Tha Sai Luat, Mae Pa, and Mae Tao, and potentially extending northward along the national highway to Mae Kasa sub-district. Additionally, forested areas are anticipated to diminish, transitioning into agricultural zones, while certain existing agricultural regions are expected to be converted into urban spaces. These findings underscore the imminent expansion of urban areas within Mae Sot, emphasizing the necessity for well-thought-out planning to ensure long-term development. [ABSTRACT FROM AUTHOR]

The advent of the big data era has provided many types of transportation datasets, such as metro smart card data, for studying residents' mobility and understanding how their mobility has been shaped and is shaping the urban space. In this paper, we use metro smart card data from two Chinese metropolises, Shanghai and Shenzhen. Five metro mobility indicators are introduced, and association rules are established to explore the mobility patterns. The proportion of people entering and exiting the station is used to measure the jobs-housing balance. It is found that the average travel distance and duration of Shanghai passengers are higher than those of Shenzhen, and the proportion of metro commuters in Shanghai is higher than that of Shenzhen. The jobs-housing spatial relationship in Shenzhen based on metro travel is more balanced than that in Shanghai. The fundamental reason for the differences between the two cities is the difference in urban morphology. Compared with the monocentric structure of Shanghai, the polycentric structure of Shenzhen results in more scattered travel hotspots and more diverse travel routes, which helps Shenzhen to have a better jobs-housing balance. This paper fills a gap in comparative research among Chinese cities based on transportation big data analysis. The results provide support for planning metro routes, adjusting urban structure and land use to form a more reasonable metro network, and balancing the jobs-housing spatial relationship. [ABSTRACT FROM AUTHOR]

The role of spatial factors in reducing carbon emissions has been receiving increasing attention from researchers; however, these impacts may involve spatial heterogeneity. In this study, 337 prefecture-level cities in China were taken as the research object. Based on national-level urban data, the global impact of urban spatial form on carbon emissions was then investigated using ordinary least squares regression, the spatial error model, and the spatial lag model. The local effects of urban spatial form on carbon emissions in different cities were then investigated using geographically weighted regression. The findings are as follows. Overall, the larger the urban built-up area and the more fragmented and decentralized the urban land use, the greater the carbon emissions. Conversely, the more centralized the urban center of a city, the lower its carbon emissions. Locally, for some Chinese cities, the total area, landscape shape index, and mean Euclidean nearest-neighbor distance were found to have significant positive effects on carbon emissions, while the largest-patch index had a significant negative impact. For all Chinese cities, the patch density was found to have no significant effect on carbon emissions. In 29% of the cities in which the landscape division index was found to significantly affect carbon emissions, this effect was positive, while it was negative in the remaining 71%. The policy implications emerging from this study lie in the need for decision-makers and urban planners to guide the shaping of low-carbon urban spatial forms. [ABSTRACT FROM AUTHOR]

Concentrations of short- and medium-chain chlorinated paraffins (SCCPs and MCCPs) were analyzed and investigated in surficial sediment collected in 2018 from ten different nearshore sites in Lake Ontario and the St. Lawrence River influenced by inputs from varying urban and historical land uses. Sites were grouped into two categories of tributary and lake according to their location. Results show that tributary sites had higher concentrations of total chlorinated paraffin (CP) than lake sites. Humber Bay, a lake site, had the highest total CP concentration (55,000 ng/gTOC) followed by Humber River, a tributary site (50,000 ng/gTOC). The lowest concentrations were found in eastern Lake Ontario and Lake St. Francis in the St. Lawrence River (540 ng/gTOC). Higher concentrations of chlorinated paraffins (CPs) were found where runoff and wastewater inputs from urban areas, current industrial activities, and population were the greatest. Levels of MCCPs were higher than SCCPs at all sites but one, Lake St. Francis. Among the SCCPs, C13 and among the MCCPs C14 were the dominant chain length alkanes, with C14 being the highest among both groups. The SCCPs and MCCPs profiles suggest that they can be used to distinguish between sites impacted by local sources vs. sites impacted by short-/long-range transport of these chemicals. [ABSTRACT FROM AUTHOR]

Environmental public interest litigation (EPIL) is a significant part of the judicial system; it is aimed at strengthening judicial protections and safeguarding public interests. Based on the quasi-natural experimental setting of China's EPIL pilot project, this study examines the impact of EPIL on the country's urban land green use efficiency (ULGUE). The findings show that effectively implementing EPIL enhances ULGUE. Specifically, this policy has led to a 6.6% increase in ULGUE in pilot cities, and its impact has grown stronger over time. Mechanism analysis results show that EPIL mainly enhances ULGUE by strengthening environmental supervision and law enforcement, by increasing public participation in environmental governance, and by promoting green innovation and industrial structure upgrades. Furthermore, heterogeneity analysis revealed that the positive effects of this policy implementation are more pronounced in resource-based cities, cities with open environmental information, and cities with high marketization. This paper provides empirical evidence for the effectiveness of environmental governance via EPIL. [ABSTRACT FROM AUTHOR]

The historical topographic map preserves rich geographic information and can provide direct assistance for the reconstruction of various geographic elements. Based on the historical data of cities throughout the Qing Dynasty, the land use scale data of cities across the country was obtained using GIS and urban perimeter conversion models. This study combines city information and city circumference records from the historical maps and archives of the late Qing Dynasty to quantitatively reconstruct the use patterns of ancient China's urban land at a spatial resolution of 1° × 1°. Uncertainty analysis of the reconstruction results was conducted using modern remote sensing image data as the validation data set. The results showed the following. (1) During the late Qing Dynasty, the total area of urban land in the various provinces and regions was 1456.015 km2. The maximum value was 208.691 km2 in Beijing-Tianjin-Hebei, the minimum value was 1.713 km2 in Qinghai, and the average value was 56.001 km2. (2) The results of grid reconstruction show that among the 398 grids with urban land distribution, the maximum value is 64.099 km2/grid, the minimum value is 0.013 km2/grid, and the average value is 3.658 km2/grid. (3) Of all the grids with urban land, the urban land grid to the west of the Hu Line accounts for 12.5% and the east to 87.5%. (4) During the late Qing Dynasty, urban land use in China was primarily concentrated in agriculturally developed areas such as the North China Plain, the Central Plains, Jiangnan, and the Sichuan-Chongqing region. (6) The results of a kernel density estimation showed that there were obviously three core areas of urban land agglomeration in China during the late Qing Dynasty: the North China Plain-Central Plains, the Jiangsu-Shanghai-Zhejiang-Anhui area, and the Sichuan-Chongqing urban core area. This study provides basic data for urban land use during historical periods and provides a basis for the quantitative reconstruction of relevant urban land data for historical archives. [ABSTRACT FROM AUTHOR]

Biogenic volatile organic compounds (BVOCs) are emitted by vegetation and react with other compounds to form ozone and secondary organic matter (OM). In regional air quality models, biogenic emissions are often calculated using a plant functional type approach, which depends on the land use category. However, over cities, the land use is urban, so trees and their emissions are not represented. Here, we develop a bottom-up inventory of urban tree biogenic emissions in which the location of trees and their characteristics are derived from the tree database of the Paris city combined with allometric equations. Biogenic emissions are then computed for each tree based on their leaf dry biomass, tree-species-dependent emission factors, and activity factors representing the effects of light and temperature. Emissions are integrated in WRF-CHIMERE air quality simulations performed over June–July 2022. Over Paris city, the urban tree emissions have a significant impact on OM, inducing an average increase in the OM of about 5 %, reaching 14 % locally during the heatwaves. Ozone concentrations increase by 1.0 % on average and by 2.4 % during heatwaves, with a local increase of up to 6 %. The concentration increase remains spatially localized over Paris, extending to the Paris suburbs in the case of ozone during heatwaves. The inclusion of urban tree emissions improves the estimation of OM concentrations compared to in situ measurements, but they are still underestimated as trees are still missing from the inventory. OM concentrations are sensitive to terpene emissions, highlighting the importance of favoring urban tree species with low-terpene emissions. [ABSTRACT FROM AUTHOR]

Introduction: Rapid uncontrolled growth of build-up areas has increasingly challenged the sustainable use of urban area simulating the growth patterns of fastest-growing cities is more necessary in dry climates, due to low ecological suitability for urban development and meeting the needs of citizens. Therefore, this research conducted aiming at predicting the expansion of urban land use in Zahedan City, Iran, which has a dry climate with an evenness landscape. Methods: Urban Expansion in Zahedan Modeled using SLEUTH (slope, land use, exclusion, urban extent, transportation and hillshade) in two historical and environmental scenarios until 2050. The input data were extracted from processing on DEM and remote sensing data and the SLEUTH model was calibrated in four stages from 1990 to 2020. Results and discussion: The results showed that the increase in Ahead extent in 2050 is more than twice as much as in 2020, and this increase was associated with a less dispersion of urban patches in the environmental scenario compared to the historical scenario. Also, the results clarified that the developable spaces are saturated in terms of slope in the east and there is the lack of urban green spaces. These results reveal the need for the attention of city managers in predicting the urban green space in the expected growth areas and compensating for the lack of vegetation cover in the former urban areas. Geographic extension of predicted urban land can be used in future environmental planning and urban developing strategies, as well as it is suggested to adopt this approach as a plan for urban planning in dry climates. [ABSTRACT FROM AUTHOR]

For many cities, the entry of financial actors into housing opens new geopolitical relations with overseas entities, including state-backed investors such as sovereign wealth funds. These transformations raise the question of the extent to which real estate enables the urbanisation of state capitalism, understood as the expansion of the state's role as promoter, supervisor, and owner of capital. Our paper answers this question through an analysis of Manchester Life, a residential real estate joint venture between Manchester City Council and the Abu Dhabi United Group, an investment firm linked to the Abu Dhabi royal family. In doing so it explores state capitalism as a form of extended urbanisation, with oil revenues from the Persian Gulf used to extract urban land rents in the Global North. It further highlights urban geopolitical implications, theorising Manchester Life as an organisational fix that reworks the geographies of value extraction while eroding democratic accountability. [ABSTRACT FROM AUTHOR]

Land use efficiency is an indicator that measures the level of regional land resource allocation and optimization. The influence of policies tends to lead to the restructuring of regional land use and allocation in urban agglomerations, and the degree of optimization in land use efficiency has become a critical component in evaluating policy performance. In light of this, it is particularly necessary to clarify the theoretical relationship between the establishment of urban agglomerations and land use efficiency and to understand their underlying mechanisms. Additionally, it is particularly important to summarize the spatial differentiation of their effects. This study constructed a theoretical analysis framework to investigate the impact of urban agglomeration establishment on land use efficiency, deriving from the policy pathways of establishing urban agglomerations. It also explored the mediating roles of public service supply, industrial agglomeration, and technological innovation, as well as the moderating effects of geographical, institutional, and economic disparities. Building upon this foundation, the approval and implementation of national-level urban agglomeration planning were treated as a quasinatural experiment for further investigation. Based on the panel data of 244 prefecture-level and above cities in China from 2009 to 2020, this study used a staggered difference-in-differences model to empirically test the impact of establishing urban agglomerations on land use efficiency. In addition, the mediating effects were tested based on three types of pathways, and heterogeneity effects were examined using a 'center-periphery' approach with multiple types of spatial distances to analyze the mechanisms of influence. The study findings were as follows: (1) The establishment of urban agglomerations had a significant and positive impact on land use efficiency, and this effect continued and was strengthened after the establishment of national-level urban agglomerations. (2) The impact of establishing national-level urban agglomerations on land use efficiency was mediated by three factors: public service provision, industrial agglomeration, and technological innovation. However, after adjusting for policy timing, only the mediating effects of industrial agglomeration and technological innovation remained, and their effects were consistent with the establishment of national-level urban agglomerations. (3) The promotion of land use efficiency by national-level urban agglomerations exhibited spatial heterogeneity, with the effect being slightly suppressed as geographic and institutional distances increased. After adjusting for policy timing, this promoting effect was shown to exhibit spatial heterogeneity, being slightly suppressed only with increasing economic distances. Based on these findings, policy recommendations are made to further strengthen regional public investment through the allocation of resources in urban clusters, align industrial agglomeration and technological innovation with rational industry layout and division of labor, and enhance regional institutional reforms and integration to achieve regional balance. [ABSTRACT FROM AUTHOR]

Achieving the goal of integrated urban–rural development is to achieve a spatially balanced development of the constituent elements of urban–rural relations in China. Rural populations and land dedicated to construction are the main components of the countryside in traditional agricultural areas; they play an important role in the development of the countryside itself in terms of urban and rural land use and in the formation of urban and rural development patterns. This study analyzes the spatial and temporal changes in rural populations and construction land at the township level, alongside assessing various forms and the extent of coupled development. Herein, we consider the role of urban–rural attractiveness and propose a framework for relationships between urban and rural development based on different forms of coupled development; a model of urban–rural forces is constructed to determine spatial patterns of urban–rural development at the township level that may transpire in the future. Our study shows that the rural population and construction land in the study area are characterized by significant spatial and temporal dynamics, indicating that traditional rural areas are in a process of rapid development and change. The results of our measurements of township-level coupling indicate that there exist four development patterns within urban–rural development: the A-type is most likely to produce new cities or satellite towns in the future and form new urban areas; the B-type is the area most likely to cease and be annexed to other villages or cities to meet building targets; the C-type comprises areas to be focused on in the future to attract populations and strictly control the growth of rural construction land areas (to avoid land transforming into the B-type); and the D-type refers to lands upon which regional township centers may develop in the future, becoming an area devoted to rural revitalization. The A-type and D-type are prioritized for the allocation of construction land, which can be contracted from types B and C. The results of this study have provided important reference for the formulation of population and construction land control policies in accordance with local conditions and the realization of integrated urban and rural development strategies. [ABSTRACT FROM AUTHOR]

The study of urban land use efficiency is of great significance for optimizing the spatial allocation of urban land, thereby promoting the intensive use of urban land and the transformation of economic development modes. Taking the Yangtze River Economic Belt (YREB) as the study object, we chose the undesirable Slacks-Based Measure (SBM) model to calculate the urban land use efficiency (ULUE). Then, we utilized the spatial correlation analysis and econometric methods to discuss its spatio-temporal features and influential factors. The results show the following: (1) The urban land use efficiency in the YREB steadily improved from 2010 to 2022, but the inter-regional efficiency gap evidently increased. (2) There is an efficiency value to be found in a multi-center network structure, and it forms a "core-periphery" distribution pattern. The high-efficiency areas in the downstream and upstream regions of the YREB are gradually increasing, while the efficiency value in the midstream area remains low. (3) The urban efficiency values have strong correlation, and they are mainly "High-High agglomeration" and "Low-Low agglomeration", and they show significant regional characteristics. (4) The economic level, industrial structure, and urbanization have obvious motivating effects on ULUE, and the positive spatial spillover effect is clear. The foreign direct investment and land finance hinder the boost of efficiency, and the latter has a negative spatial spillover role on the ULUE in the downstream cities. [ABSTRACT FROM AUTHOR]

The urban agglomeration in the middle reaches of the Yangtze river (MYRUA) is the second largest national urban agglomeration in China, with an excellent ecological foundation. As the construction of urban agglomerations accelerated towards both extension and connotation, land prices of various cities rose, and the contradiction in the demand and utilization of land resources became acute. Therefore, exploring the spatiotemporal evolution and driving mechanisms of land green utilization efficiency within MYRUA had positive significance. This article focused on 28 prefecture‐level cities in the MYRUA and measured their urban land green use efficiency (ULGUE) from 2006 to 2018 using the super slack‐based measure model with undesirable output (Super‐SBM‐U). Then, the improved Fixed Malmquist–Luenberger index was developed to improve the accuracy of dynamic efficiency analysis. Further, the internal and external driving factors were explored using the index decomposition and the spatial error model with fixed effects. The results showed that the land use mode of the urban agglomeration in the middle reaches of the Yangtze River was relatively green, but efficiency gaps still existed. Although the green total factor productivity of the whole cluster has improved by 3.30% a year on average, the excessive pursuit of technological innovation has led to the stagnation of urban land management in various cities, and there are numerous inherent contradictions in urban development. But at the same time, feasible economic development directions, industrial upgrading, high‐quality employees, land marketization, and positive environmental behaviors are conducive to the green and sustainable development of ULGUE. This article depicted the internal land use characteristics of the MYRUA from the perspective of spatiotemporal evolution and efficiency, providing a theoretical reference for the green development and coordinated management of urban agglomeration land. Recommendations for Resource Managers: Natural features and socioeconomic heterogeneity lead to a significant discrepancy in the green efficiency of urban land use. Thus, they should be regarded as the scientific basis of policy design for an entire urban agglomeration.Improving regional connections is vital for enhancing the exemplary and radiation effects of core cities on green land use.While the importance of technology has been stressed, the management of related systems should keep pace to provide sustainable forces for the development of urban agglomerations. [ABSTRACT FROM AUTHOR]

Driven by the goal of achieving sustainable development and carbon neutrality. Addressing environmental pollution and remediating land damage have become critical challenges in resource-based cities and regions with low land use efficiency. As a response, this study focuses on the 23 provinces where China's coal resource-based cities are situated. Utilizing data from 2014 to 2020, this research employs the SBM-Undesirable model, which considers undesirable outputs in efficiency calculations, and the Tobit regression test. It aims to explore the spatio-temporal variations in industrial transformation within resource-based cities and its impact on the efficiency of green space utilization. Furthermore, it analyzes the characteristics and the extent of the influence of factors such as industrial structure adjustments on urban land use efficiency, maximizing the output of land as a factor of production. The results show that: (1) Over the 7-year period studied, China consistently made nationwide adjustments to land area and land use structure to meet the needs of urban development (2) The regression test results show that the industrial transformation of resource-based cities can promote the improvement of green space utilization efficiency. The positive influence coefficient is 0.064 and is significant at a 1% level. (3) Environmental regulation, government expenditure, international trade, and green cover play a positive role in promoting green land use. The study provides valuable insights for policymakers and urban planners seeking to foster sustainable development in resource-based cities. [ABSTRACT FROM AUTHOR]

Jakarta holds the distinction of being the largest capital city among ASEAN countries and ranks as the second-largest metropolitan area in the world, following Tokyo. Despite numerous studies examining the diverse urban land use and land cover patterns within the city, the recent state of urban green spaces has not been adequately assessed and mapped precisely. Most previous studies have primarily focused on urban built-up areas and manmade structures. In this research, the first-ever detailed map of Jakarta's urban green spaces as of 2023 was generated, with a resolution of three meters. This study employed a combination of supervised classification and evaluated two machine learning algorithms to achieve the highest accuracy possible. To achieve this, various satellite images were utilized, including VV and VH polarizations from Sentinel-1, multiple bands from Sentinel-2, and eight bands from Planet. The Planet data were subsequently transformed into the Red-Edge Triangulated Vegetation Index and Red-Edge Triangulated Wetness Index. The data training and testing samples for urban green spaces were obtained using the Street View images available on Google Maps. The results revealed that using the Random Forest classifier algorithm and only eight bands of Planet images achieved an accuracy rate of 84.9%, while a combination of multiple images achieved an impressive 95.9% accuracy rate. Jakarta's urban areas cover approximately 33.2% of green spaces. This study provides unprecedented insights into the type, size, and spatial distribution of Jakarta's urban green spaces, enabling urban residents and stakeholders to explore and promote healthier living and better manage these green areas. Additionally, a previously unexplored concept, Jakarta's urban green belt, is introduced. [ABSTRACT FROM AUTHOR]

Soils in residential areas of cities are heavily degraded, and the environmentally protective and formative functions are instead realized by the soils in recreational areas (city parks, etc.). The study aimed to analyse the influence of functional land use and the level of anthropogenic impact on the properties of anthropogenic horizons (Au and At) in the city of Volgograd, Russia. In this study, we analysed 50 soil samples from the city's recreational and residential functional areas under field and laboratory conditions. The study evaluated the morphological aspects (thickness, colour, structure, and presence of artefacts), physical properties (bulk density, texture), and chemical properties (pHwater, salt content, CaCO3, Corg, SOCstoc) of the soils. The anthropogenic Au horizons in residential areas exhibited a clumpy structure, numerous artefacts, and significant compaction. Conversely, the soils in recreational zones contained fewer anthropogenic artefacts, with the At horizons characterized by a lumpy structure. The anthropogenic horizons' median and mean property values in the functional zones showed significant differences. The acid-alkaline properties of the studied horizons were weakly alkaline in recreational areas and alkaline in residential areas. A common feature of all anthropogenic horizons was the variability in chemical, physical, and morphological properties depending on the functional zone and level of anthropogenic load. The indicators of a specific level of anthropogenic impact on urban landscapes included horizon thickness, Corg content, colour, and structure. [ABSTRACT FROM AUTHOR]

Descriptive scenarios about the possible evolution of land use in our cities are essential instruments in urban planning. Although the simulation of these scenarios has enormous potential, further characterization is needed in order to be able to evaluate and compare them so as to provide more effective support for public policy. One of the most commonly used tools for assessing these scenarios is spatial moving‐window metrics, a useful mechanism for extracting accurate information from simulated land‐use maps on urban diversity and urban growth patterns. This article seeks to explore this question further and has two main aims. First, to develop and implement vSHEI and vLEI, two multiscale composition and configuration vector moving‐window metrics for calculating urban diversity and urban growth patterns. Second, to test these metrics using the spatially explicit simulation of three prospective scenarios in the Henares Corridor (Spain), comparing the results and analyzing how well the scenario narratives match their spatial configuration, as measured using vSHEI and vLEI. Via the implementation of vSHEI and vLEI, we obtained urban diversity and urban expansion values at a local level, offering more precise and more realistic, mappable information on the composition and configuration of urban land use than that provided by raster metrics or by vector Patch‐Matrix model metrics. We also used these metrics to test whether the simulated scenarios matched their description in the narrative storylines. Our results demonstrate the potential of vector moving‐window metrics for characterizing the urban patterns that might develop under different scenarios at the parcel level. [ABSTRACT FROM AUTHOR]

Current information on urban land use and surface cover is derived from the land classification of cities, facilitating accurate future urban planning. Key insights are driven by multi-year remote sensing data. These data, when analyzed, produce high-resolution changes on the Earth's surface. In this context, publicly accessible Urban Atlas data are employed for the high-precision and high-resolution classification and monitoring of terrestrial surfaces. These datasets, which are useful for preserving natural resources, guiding spatial developments, and mitigating pollution, are crucial for monitoring changes and managing cities. This research aims to analyze and contrast land use and land cover (LULC) changes in Gaziantep (Turkey) between 2010 and 2018 using Urban Atlas data, and to investigate correlations between the city's statistical data and LULC changes. Gaziantep's urban dynamics were analyzed using Urban Atlas datasets from 2010 to 2015 and 2012 to 2018, the latter part of Copernicus, the European Earth Observation Programme. To understand the impact of LULC changes on urban landscapes, people, and the environment, official environmental and demographic statistics spanning four years were sourced and studied. The findings reveal a trend of agricultural and vacant lands evolving into residential and industrial zones, with such changes likely to increase in the near future, given the growth of building zones. While some land classes have shown consistent area values annually, residential and industrial zones have expanded in response to housing and employment demands. The most significant alterations have occurred in the last three years. Shifts in urban configurations align closely with migratory patterns, reflecting notable variations in factors like population, consumption, and pollution. [ABSTRACT FROM AUTHOR]