Block-level Carbon Emission Control Unit Constructing and Low-Carbon Planning Strategies for Territorial Spatial Planning

Objective Cities are the primary spatial carriers of human production and living activities, as well as concentrated areas of carbon emissions. Therefore, building low-carbon cities is crucial for advancing the goals of "carbon peaking" and "carbon neutrality." Given the spatial heterogeneity of urban morphology and carbon emissions, this study aims to develop fine-scale spatial assessment tools for carbon emissions. It analyzes the impact of urban morphology on carbon emissions and investigates the integration of emission models with low-carbon planning strategies within the national spatial planning framework. The ultimate goal is to provide informational support and a decision-making foundation for low-carbon territorial spatial planning and digital green governance. By understanding the spatial distribution and sources of carbon emissions, urban planners can devise more effective interventions to mitigate urban carbon emissions. This study also addresses the gap between theoretical models and practical applications in urban planning and policymaking, offering a framework adaptable to diverse urban contexts. Methods Based on the Local Climate Zone (LCZ) framework, this study developed LCZ classification maps and a series of urban morphology analysis maps for Guangzhou. Furthermore, a refined spatial carbon emission assessment map was constructed using a combination of top-down and bottom-up approaches. The approach includes the following steps: First, by integrating planning data from the GIS geographic information platform, such as buildings, streets, terrain, and land use, along with remote sensing and meteorological data, key urban form parameters, including building coverage ratio, building volume density, sky view factor, and street height-to-width ratio, were calculated at a grid scale of 300m * 300m. These parameters were then used to develop LCZ classification and urban morphology maps, providing a data foundation for spatial carbon emission modelling. Subsequently, by calculating proxy indicators such as building volume density, road area, agricultural land area, point of interest density, and population density, and combining them with energy consumption data from urban statistical yearbooks, the study spatially distributed carbon emissions across five sectors: industrial, transport, residential, commercial, and agricultural. A comprehensive city carbon emission map was generated. Finally, statistical analysis was conducted to examine the spatial correlations and variations in carbon emissions across different administrative units, and hotspot analysis was used to identify statistically significant carbon emission hotspots and cold spots. Results The findings reveal several key insights: 1) LCZ classification maps and carbon emission maps facilitate the identification of emission hotspots. Central business districts and industrial zones have the highest emissions due to dense construction and high economic activity, whereas suburban and peri-urban areas with more open spaces and vegetation exhibit lower emission levels. 2) Industrial areas contribute the most to carbon emissions, followed by the transportation and residential sectors. Public services and agricultural sectors, while significant, have a relatively smaller impact on overall emissions. 3) Industrial carbon emission areas are primarily in peripheral industrial zones, corresponding to LCZ9 (Sparsely built) and LCZ10 (Heavy Industry. Transportation carbon emission areas are mainly in Baiyun, Nansha, and central areas with dense road networks, corresponding to LCZ9, LCZ10, LCZE (Bare rock, Pave), and LCZF (Bare soil). Residential, public service, and commercial carbon emissions are dominant in central districts like Liwan and Yuexiu, corresponding to high-density LCZ types. Agricultural carbon emission areas are located in peripheral regions, corresponding to LCZA&B (Dense trees & scattered trees) and LCZC&D (Bush, shrub & low plants). 4) Urban design significantly influences carbon emissions. Areas with well-planned public transportation networks and pedestrian-friendly infrastructure report lower emissions from transportation. Green roofs, urban parks, and water bodies mitigate the urban heat island effect, reducing energy demand for cooling and thereby lowering carbon emissions. 5) There is a strong correlation between the spatial distribution of carbon emissions and urban morphology. High-density areas with concentrated human activity tend to have higher emissions, while areas with lower building density and more green space exhibit reduced emissions. 6) The model provides practical guidance for urban planners in designing low-carbon cities. Key strategies include expanding green spaces, improving public transportation, and promoting energy-efficient buildings to effectively reduce carbon emissions. Conclusion The carbon emission assessment model based on LCZ effectively translates planning language into actionable insights, integrating into the multi-level transmission, full-process implementation, and procedural development of the territorial spatial planning system. This approach offers valuable insights into the digital governance of territorial spatial planning and the transformation towards urban green development. Ultimately, this research contributes to the literature on climate change, the built environment, and public health. It emphasizes the importance of comprehensive urban planning that incorporates both environmental sustainability and public health considerations. As the world continues to grapple with climate change challenges, this research highlights the necessity of interdisciplinary approaches to understand and manage the complex interactions between the environment, human behaviour, and health outcomes. By leveraging these insights, urban planners and policymakers can work towards creating built environments that are resilient to climate change and promote the well-being of residents.