An accurate carbon emissions map is of great significance for urban planning to reduce carbon emissions, mitigate the heat island effect, and avoid the impact of high temperatures on human health. However, little research has focused on carbon emissions maps at the land patch level, which makes poor integration with small and medium-sized urban planning based on land patches. In this study, a vectorization method for spatial allocation of carbon emissions at the land patch level was proposed. The vector maps and spatial autocorrelation of carbon emissions in Zhangdian City, China were explored using multi-source data. In addition, the differences between different streets were analyzed, and the carbon emissions ratio of the land patch was compared. The results show that the vector carbon emissions map can help identify the key carbon reduction land patches and the impact factors of carbon emissions. The vector maps of Zhangdian City show that in 2021, the total carbon emissions and carbon absorptions were 4.76 × 10⁹kg and 4.28 × 10⁶kg respectively. Among them, industrial land accounted for 70.16% of carbon emissions, mainly concentrated in three industrial towns. Forest land carbon absorption accounted for 98.56%, mainly concentrated in the peripheral streets away from urban areas. The Moran's I of land patch level carbon emissions was 0.138, showing a significant positive spatial correlation. The proportion of land patches is an important factor in determining carbon emissions, and the adjustment of industrial structure is the most critical factor in reducing carbon emissions. The results achieved can better help governments develop different carbon reduction strategies, mitigate the heat island effect, and support low-carbon and health-oriented urban planning.

Evaluating the effects of built environment factors (BEF) on residential land carbon emissions (RLCE) is an effective way to reduce RLCE and promote low-carbon development from the perspective of urban planning. In this study, the Grey correlation analysis method and Universal global optimization method were proposed to explore the effects of BEF on RLCE using advanced metering infrastructure (AMI) data in Zibo, a representative resource-based city in China. The results indicated that RLCE can be significantly affected by BEF such as intensity, density, morphology, and land. The morphology is the most critical BEF in reducing RLCE. Among them, the building height (BH) and building shape coefficient (BSC) had positive effects on RLCE, while the high-rise buildings ratio (HRBR) and RLCE decreased first and then increased. The R² of BH, BSC, and HRBR are 0.684, 0.754, and 0.699. The land had limited effects in reducing RLCE, and the R² of the land construction time (LCT) is only 0.075, which has the least effect on RLCE. The results suggest that urban design based on BEF optimization would be effective in reducing the RLCE.

The global climate crisis is escalating, and how to reduce land use carbon emission (LUCE) while promoting social and economic development is a global issue. The purpose of this study was to investigate the spatio-temporal evolution characteristics and influencing factors of LUCE at the county scale. To accomplish this goal, based on Zibo County land use data and societal energy consumption statistics, for predicting the net LUCE in 2010, 2015, and 2020. GIS spatial analysis and spatial autocorrelation model were utilized to investigate the spatio-temporal evolution characteristics of LUCE. The geographical and temporal weighted regression (GTWR) model was used to investigate the influencing factors and spatial differences. The findings demonstrate that: (1) the rate of land use change in Zibo City decreased between 2010 and 2020, with the overall land use motivation falling from 0.14% to 0.09%. The area of arable land, forest land, and grassland decreased, while the amount of water, developed land, and unutilized land increased. Between 2010 and 2020, net carbon emissions in Zibo City increased significantly, from 3.011 × 10⁷tC to 3.911 × 10⁷tC. The spatial distribution of LUCE followed a clear pattern of “elevated in the east and diminished in the west, elevated in the south and diminished in the north.” The spatial agglomeration characteristics of LUCE are obvious, and the overall trend of the Moran I value is falling, from 0.219 to 0.212. The elements that determine LUCE vary greatly by location, with the most major influences being, in descending order, energy consumption per unit of GDP, urbanization rate, land-use efficiency, and population size. The energy consumption per unit of GDP has the greatest impact on Linzi District, with coefficients ranging from 55.4 to 211.5. The study clearly depicts the spatio-temporal distribution of carbon emissions resulting from land use in Zibo City and the factors that contribute to them. Simultaneously, it provides a scientific framework for improving land use structure and implementing low-carbon programs throughout the region.

The global climate crisis is escalating, and urban living Space (ULS) is a significant contributor to carbon emissions. How to improve the carbon suitability of ULS while promoting social and economic development is a global issue. This study aims to develop an evaluation system for comparing and analyzing carbon suitability inequality and spatial differences in different areas. To achieve this goal, an urban living space carbon suitability index (ULS-CSI) based on spatial organizational index (SOI) has been proposed. The ULS-CSI was calculated at the area scale in Tianjin using information from the Tianjin Land Use Database in 2021. The carbon emissions coefficient method was used to calculate the urban living space carbon emissions (ULSCE). Moran’I and LISA analysis were used to quantify the spatial differences of ULS-CSI. The results showed that the residential living area (RLA) carbon emissions was the highest at the area scale, with carbon emissions of 1.14 × 10¹¹ kg, accounting for 33.74%. The green space leisure area (GLA) carbon absorption was the highest at the area scale, with carbon absorption of 5.76 × 10⁵ kg, accounting for 32.33%. SOI in different areas have spatial heterogeneity as the SOI such as building area, road network density and land use characteristics are significantly different in different areas. Areas with superior CSI were primarily situated in Heping, Hexi, Nankai, and Beichen, accounting for 83.90%. Conversely, areas under the basic CSI threshold included Xiqing, Jinnan, and Dongli, accounting for 16.10%. Spatial characteristics of ULS-CSI in Tianjin portrayed a significant spatial positive correlation, indicating the highest autocorrelation degree of CSI at 500 m, with a Moran ’I value of 0.1733. Although these findings reflect the spatial characteristics of ULS-CSI and the SOI affecting the ULS-CSI at area scale, more perfect data are needed to reflect the complexity of structural factors affecting ULS-CSI at area scale. This study is helpful for urban planning to develop differentiated carbon reduction strategies and promote low-carbon and healthy urban development.

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.

Against the backdrop of the intensifying global climate crisis, urban construction land (UCL), as a major source of carbon emissions, faces the severe challenge of balancing emissions reduction and development in its low-carbon transformation. This study is dedicated to filling the theoretical and methodological gap in the refined assessment of urban construction land carbon effects (UCLCE) spatial heterogeneity among regions, and proposes and validates an innovative block-scale prediction framework. To achieve this goal, this study takes the central urban area of Changxing, Zhejiang Province, as the study area and establishes a BP neural network model for predicting UCLCE based on multi-source data such as building energy consumption and built environment elements (BEF). The results demonstrate that the BP neural network model effectively predicts the different types of UCLCE, with an average error rate of 30.10%. (1) The total effect and intensity effect exhibit different trends in the study area, and a carbon effect table for different types of UCL is established. (2) The spatial distribution characteristics of UCLCE reveal a distinct reverse-L pattern (“┙”-shaped layout) with positive spatial correlation (Moran’s I = 0.11, p < 0.001). (3) The model’s core practical value lies in enabling forward-looking assessment of carbon effects in urban planning schemes and precise quantification of emissions reduction benefits. Optimization trials on representative blocks achieve up to 25.45% carbon reduction. This study provides theoretical foundations for understanding UCLCE spatial heterogeneity while delivering scientifically grounded tools for diagnosing built environment issues and advancing low-carbon optimization in urban renewal contexts. These contributions carry significant theoretical and practical implications.