Construction of and Low-Carbon Optimization Strategies for Block-Level Carbon Emission Control Unit - Wenjie LI; Yingsheng ZHENG; Qingfang ZHANG; Qiuyun ZENG (Fengjing Yuanlin)
ObjectiveCities 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 carbon peaking and carbon neutrality goals. Given the spatial heterogeneity of urban morphology and carbon emissions, this research aims to develop fine-scale spatial assessment tools for carbon emissions. The research analyzes the impact of urban morphology on carbon emissions and investigates the integration of emission models with low-carbon planning strategies within the territorial 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. The research also addresses the gap between theoretical models and practical applications in urban planning and policymaking, and offers a framework adaptable to diverse urban contexts.MethodsBased on the local climate zone (LCZ) framework, the research develops LCZ classification maps and a series of urban morphology analysis maps for Guangzhou. Furthermore, a refined spatial carbon emission assessment map is constructed using a combination of top-down and bottom-up approaches, which involves the following steps. First, by integrating planning data from the ArcGIS geographic information platform, such as data on buildings, streets, terrain, and land use, along with remote sensing and meteorological data, key urban morphology parameters, including building coverage ratio, building volume density, sky view factor, and street aspect ratio, are calculated at a grid scale of 300 m * 300 m. These parameters are 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 research spatially distributes carbon emissions across five sectors, namely the industrial, transport, residential and public services, commercial, and agricultural sectors. Based on this, a comprehensive city carbon emission map is generated. Finally, a statistical analysis is conducted to examine the spatial correlations and variations in carbon emissions across different administrative units, and a hotspot analysis is conducted to identify statistically significant carbon emission hotspots and coldspots.ResultsThe 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) The industrial sector contributes the most to carbon emissions, followed by the transport and the residential and public service sectors, and the agricultural sector has a relatively smaller but still significant impact on overall emissions. 3) Industrial carbon emission areas are primarily in peripheral industrial zones, corresponding to LCZ 9 (low-density built-up area) and LCZ 10 (heavy industrial area). Transportation carbon emission areas are mainly in Baiyun and Nansha districts, as well as central urban areas with dense road networks, corresponding to LCZ 9, LCZ 10, LCZ E (bare rock and pavement area), and LCZ F (bare soil area). Carbon emissions from the residential and public services sector and the commercial sector are dominant in central urban areas like Liwan and Yuexiu districts, corresponding to high-density LCZ types. Agricultural carbon emission areas are located in peripheral regions, corresponding to LCZ A&B (dense trees & scattered trees) and LCZ C&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 emission models adopted may provide a 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.ConclusionThe carbon emission assessment model based on LCZ effectively translates planning language into actionable insights, thus 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, built environment, and public health. The research 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, the research highlights the necessity of interdisciplinary approaches to understanding and managing 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.