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To address the pressure of emissions reduction in urban residential blocks (RBs), this study takes 99 micro-scale RBs in Hongqiao District, Tianjin as the objects, aiming to reveal the driving mechanism of built environmental factors (BEF) on residential blocks carbon emissions (RBCE) and explore planning strategies that balance carbon reduction and health benefits. By integrating spatial statistical analysis and high-precision machine learning models, the system has systematically revealed the spatio-temporal evolution laws, spatial differentiation characteristics and driving mechanisms of BEF on RBCE. Key findings include: (1) From 2021 to 2023, both the RBCE, residential blocks carbon emissions intensity (RBCEI), and average household carbon emissions (RBCE-AH) showed a "first rise then fall" fluctuation, with an overall 5.7% increase, signaling sustained emissions reduction pressure. (2) High emissions areas are spatially concentrated and contagious, while low carbon units are mostly peripheral. Spatial autocorrelation analysis indicates a significant positive correlation and a west-south clustering pattern. (3) Land area (LA) is the main emissions affecting factor, followed by green space ratio (GSR) and Land use mixing degree (LMD), whose inhibitory effect exceeds that of traditional high-intensity development indicators. (4) Targeted planning strategies such as strictly controlling land use expansion, improving GSR, and promoting functional combination were proposed. At the same time, it was suggested that in the future, the heterogeneity of building types and more three-dimensional morphological indicators should be incorporated into the BEF index system, and combined with more refined coupling models, their influence paths should be quantitatively analyzed. These strategies not only provide a basis for the implementation of macro emissions reduction policies, but also offer solutions for micro action plans centered on residents'mental health and cardiopulmonary system protection. Overall, this study provides a scientific basis for low carbon RBs planning and renewal that balances carbon reduction with health benefits.
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http://dx.doi.org/10.3389/fpubh.2025.1645402 | DOI Listing |
Environ Plan B Urban Anal City Sci
March 2025
Department of Landscape Architecture and Urban Planning, Texas A&M University.
Urban green space disparities persist amid rapid urbanization, widening the supply-demand gap between parks and developed area. Population density is a critical determinant in estimating park visitors, defining suitable park locations, and allocating facilities for park accessibility. Conventionally, population density data were used as a foundational basis for urban green space planning decisions, often derived from sources like the US Census Bureau, primarily reflecting "nighttime residential" distribution.
View Article and Find Full Text PDFPurpose: Cancer control relies on the identification of populations at risk (hotspots) of new or late-stage cancer diagnoses. However, the extent to which hotspots differ between cancer sites or between outcome measures has been poorly characterized. We sought to determine the geospatial heterogeneity of hotspots of breast, colorectal, and lung cancer incidence and late-stage diagnoses.
View Article and Find Full Text PDFFront Public Health
September 2025
School of Architecture and Urban Planning, Shandong Jianzhu University, Jinan, China.
To address the pressure of emissions reduction in urban residential blocks (RBs), this study takes 99 micro-scale RBs in Hongqiao District, Tianjin as the objects, aiming to reveal the driving mechanism of built environmental factors (BEF) on residential blocks carbon emissions (RBCE) and explore planning strategies that balance carbon reduction and health benefits. By integrating spatial statistical analysis and high-precision machine learning models, the system has systematically revealed the spatio-temporal evolution laws, spatial differentiation characteristics and driving mechanisms of BEF on RBCE. Key findings include: (1) From 2021 to 2023, both the RBCE, residential blocks carbon emissions intensity (RBCEI), and average household carbon emissions (RBCE-AH) showed a "first rise then fall" fluctuation, with an overall 5.
View Article and Find Full Text PDFObjective: This study leveraged residence- and neighborhood-specific socio-environmental data linked to population-wide healthcare data to characterize risk for pediatric hospitalization for every residential address in Cincinnati, Ohio.
Materials And Methods: We linked hospitalization data (07/01/2016-06/30/2022) to parcel-level housing data from the Hamilton County Auditor and Cincinnati Department of Buildings & Inspections and block-level crime data from the Cincinnati Police Department. Addresses were localized to 2010 census tracts to join variables from the U.
Sci Data
August 2025
Environmental Defense Fund, New York, NY, USA.
Flooding is the most common and damaging natural disaster in the United States (US), and understanding the number of people at risk of flooding is critical information for planning. The dataset presented here uses publicly available census and building footprint data to improve upon previous estimates of the number of people and housing units in fluvial or coastal flood hazard areas in the contiguous US. To calculate the population and housing unit estimates, the ratio of total residential building footprint area that intersects high flood hazard areas is multiplied by 2020 Decennial Census block counts.
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