Spatio-temporal Correlation Between Green Space Landscape Pattern and Carbon Emission in Three Major Coastal Urban Agglomerations

Rapid urbanization has altered the land use pattern, reducing urban green space and increasing carbon emissions, and it is critical to scientifically examine the interaction mechanism between green space and carbon emissions in order to drive low-carbon urban development. Using Nanjing as an example, this study examined the spatiotemporal evolution characteristics of urban green space patterns and carbon emissions between 2000 and 2020. Carbon emissions at the city and county levels were estimated with great precision using a gray BP neural network model and a downscaling decomposition method. Using urban green space landscape pattern indices and geographic detectors, significant driving factors were discovered and their impact on carbon emissions examined. The results show the following: (1) Carbon emissions are mostly influenced by socioeconomic factors, and the gray BP neural network model (R2 = 0.9619, MAPE = 1.68%) can predict outcomes accurately. (2) Between 2000 and 2020, Nanjing’s overall carbon emissions increased by 118.9%, demonstrating a “core–periphery” pattern of spatial divergence, with significant emissions from industrial districts and emission reductions in the central urban region. (3) The urban green space exhibits “quantity decreasing and quality increasing” characteristics, with the total area falling by 4.84% but the structure optimized to form a networked pattern with huge ecological patches as the backbone. (4) The primary drivers are the LPI, COHESION, and AI. This study reveals the complex relationship mechanism between the spatial configuration of urban green space and carbon emissions and, based on the results, proposes a green space optimization framework with three dimensions, protection of core ecological patches, enhancement of connectivity through ecological corridors, and implementation of low-carbon maintenance measures, which will provide a scientific basis for the planning of urban green space and the construction of low-carbon cities in the Yangtze River Delta region.

Can new-type urbanization reduce urban building carbon emissions? New evidence from China

Carbon emissions increase the risk of climate change. As one of the primary sources of carbon emissions, road traffic faces a significant challenge in terms of reducing carbon emissions. Many studies have been conducted to examine the impacts of cities on carbon emissions from the perspectives of urbanization, population size, and economics. However, a detailed understanding of the relationship between road traffic and urban carbon emissions is lacking due to the lack of a reasonable set of road traffic metrics. Furthermore, there have been fewer studies that have conducted cluster analyses of the impact factors, which will be supplemented in this research. We established 10 impact metrics, including the highway network system, city road network system, public transit system, and land use system of streets and transportation, using 117 county-level cities in Hebei Province as the study area, which is one of the regions in China with the most acute conflicts between economic development and the environment. We built an ordinary least squares (OLS) model, a spatial lag model (SLM), a spatial error model (SEM), a spatial Durbin model (SDM), and a geographically weighted regression (GWR) model, and performed a cluster analysis on the key metrics. The results are as follows: (1) The difference in spatial distribution of urban land-average carbon emissions is obvious, highly concentrated in the areas surrounding Beijing and Tianjin. (2) The GWR model has a higher R2 and a lower AICc than global models (OLS, SLM, SEM, and SDM) and performs better when analyzing the impact mechanism. (3) Highway network density, city road length, and density of the public transit network have significant effects on urban land-average carbon emissions, whereas the street and transportation land use systems have no significant effect, which indicates that the highway network and public transit systems should be prioritized. (4) The GWR model results show that the impact of the four metrics on the urban land-average carbon emissions exhibits clear spatial heterogeneity with a significant piecewise spatial distribution pattern. The highway network density has a relatively large impact on the northern region. The northwest is more affected by the density of the public transit network. The southwest is most impacted by the length of city roads. (5) The study area is divided into four distinct characteristic areas: the highway network dominant impact area, the public transit dominant impact area, the city road network dominant impact area, and the multi-factor joint impact area. Different traffic optimization strategies are proposed for different areas.

The relationships between urban landscape patterns and fine particulate pollution in China: A multiscale investigation using a geographically weighted regression model

Green infrastructure (GI) plays a pivotal role in contemporary urban infrastructure. Green infrastructure investment (GII) provides a fresh perspective for controlling urban carbon emissions in the context of global climate change. Based on theoretical analysis, we employed panel data from Chinese cities to examine the effects and operating mechanisms of GII on urban carbon emissions. The research reveals that the incremental GII can notably decrease urban carbon emissions, and various robustness tests and endogeneity checks corroborate this finding. However, when considering the cumulative effect, the GII stocks do not appear to influence urban carbon emissions; GII mitigates urban carbon emissions by drawing in pollution control talents, improving the efficiency of household waste treatment, increasing urban green spaces, and heightening public attention to the environment. Relative to cities in the central-western region, northern cities, smaller cities, resource-based cities, smart pilot cities, and cities with a lesser environmental emphasis, GII is more effective in curbing carbon emissions in eastern cities, southern cities, larger cities, non-resource-intensive cities, cities not in the smart pilot initiative, and cities with a stronger environmental focus. This research enhances the understanding of GI’s environmental outcomes and the determinants of urban carbon emissions from an investment viewpoint. It also dissects the four operative mechanisms through which GII lowers urban carbon emissions, offering a novel interpretation of GII for the variance in carbon emission levels across cities with diverse traits.

Quantification of green space patterns is essential for the monitoring and assessment of ecological consequences of urbanization. Using RS, GIS and landscape structure analysis program FRAGSTATS, based on the urban green space type map by Quickbird image of Shenyang in 2008, combined gradient analysis with landscape metrics, this paper aimed to quantify the spatial pattern of urban green space in Shenyang city and investigate the effects of spatial extend in analyzing urban green space landscape pattern. The patch density, landscape shape index showed significant gradient characteristics, while the large patch index, Shannons diversity index, and the percentage of landscape, landscape aggregation index were in adverse. The landscape metrics had different variation patterns with the variation of spatial extend. The results of transect analysis with landscape-level metrics showed that the spatial pattern of urban green space could be reliably quantified using landscape metrics with a gradient analysis approach. It was suggested that a spatial extent of 5km might be more appropriate for urban green space pattern analysis by landscape metrics changing among different window sizes.

Scale matters: How spatial resolution impacts remote sensing based urban green space mapping?

Decoupling relationship between urban land use morphology and carbon emissions: Evidence from the Yangtze River Delta Region, China

Implementing carbon mitigation through urban spatial optimisation is a possible solution for alleviating global warming. However, the relationship between urban carbon emissions and urban spatial structure has not been well clarified, as adequate mapping of high-spatial-resolution urban carbon emissions from different sectors (particularly residential sectors), a precondition to solving the problem, has yet to be achieved. This study proposes a hybrid method of mapping the spatial distribution of urban residential carbon emissions on a 1 km × 1 km scale using multi-source data and exemplifies it via a case study of the Chinese city of Suzhou. The purpose of using this method is to differentiate residential carbon emissions by commuter population and home-based population, as the time they spend at home differs. The mobile signalling data of Suzhou were used to identify commuter and home-based populations. The number and spatial distribution of these two groups were then calibrated by referring to land use and O-D data. Using calibrated data, the proportion of electricity consumed by the two groups in different residential districts across the city was calculated. Total urban residential carbon emissions were then proportionally allocated to 1 km × 1 km grids. By validating estimated data against the data from the Statistical Yearbook, we found that the proximity level is higher than 93%. Furthermore, comparing these outcomes against the results estimated by using NTL data and the size of the identified population as the proxy data, it was observed that the results estimated by the hybrid method are of higher accuracy and stability.

ObjectiveThis study recalculates the carbon emissions of urban and rural residents in China, analyzing the dynamic evolution trends of urban and rural carbon emissions. It explores the spatial spillover effects centered around the inequality in carbon emissions between urban and rural areas.MethodsThe study calculates the carbon emissions of urban and rural residents in each province based on the IPCC method. Non-parametric kernel density estimation is employed to depict the dynamic evolution characteristics of national, urban, and rural carbon emissions. The Theil Index is used to measure the disparities in urban and rural carbon emissions in major strategic regions, further applying the Theil Index to evaluate the inequality of urban and rural carbon emissions across provinces. This helps identify the driving factors affecting the inequality of urban and rural carbon emissions and their spatio-temporal effects.FindingCarbon emissions from urban and rural residents in China present a divergent development pattern. Urban emissions have increased, with inter-provincial disparities widening; rural emissions tend to stabilize, with slight growth in inter-provincial gaps. The overall inequality of carbon emissions in various regions of China experiences a three-phase journey of rise, decline, and stabilization. Urban inequality first increases then decreases, while rural inequality gradually lessens, showing clear regional and urban-rural differences. Market and government factors significantly impact the inequality of urban and rural carbon emissions. The development of the digital economy aids in reducing inequality and generates significant spatial spillover effects. The relationship between economic development level and carbon emission inequality is U-shaped. Industrial structure optimization can reduce urban-rural inequality, but its spatial spillover effect is not significant. Government intervention has limited effects, while environmental regulations may increase inequality. Opening up to the outside world helps reduce inequality, and the impact of population density is complex.

With the acceleration of urbanization, the heat island effect, as a prominent feature of urban climate, has attracted more attention. Differences in urban landscape patterns have an essential impact on the urban thermal environment. The objective of the study is to examine the impact of urban landscape types and patterns on surface temperature. Taking Zhengzhou City, China, as an example, using Google Earth remote sensing images, an urban landscape type map was created, and landscape indices were calculated. The land surface temperature (LST) of the study area was retrieved by the Landsat-8 thermal infrared band. Correlation analysis indicated that the relationships between urban landscape patterns and the thermal environment were as follows: (i) The scale indices (percentage of landscape (PLAND), largest patch index (LPI), edge density (ED), patch density (PD)) of urban landscape types with cooling effect (water body, riverfront area, park, high-rise building) were significantly negative correlated with mean LST of each partition. (ii) Conversely, there were significant positive correlations between the PLAND and LPI of landscape types with warming effect (block, development land, railway land) and the LST of the partition. (iii) The DIVISION index of the four kinds of landscapes with cooling effect was highly positively correlated with LST, and the DIVISION and SPLIT indices of the three kinds of landscapes with warming effect displayed a remarkable negative relationship with LST. Therefore, under the condition of scale control, integrated distribution of landscape with cooling effect, scattered distribution of landscape with warming effect, and reduced connectivity of landscape with warming effect will contribute to effectively alleviating the formation of urban heat islands.

Urban green space and urban landscape patterns are of great significance to the sustainable development for urban ecosystems. Spatial statistics such as Moran’s I indicator can only reveal the spatial autocorrelation of urban green space or urban landscape itself, while the dynamic changes of urban green space and urban landscape in spatial correlation and spatial heterogeneity must be investigated under spatio-temporal contexts, with possible driving factors such as urban climate and urbanization process taken into account. The purpose of this paper was to study the dynamics of urban greenness patterns using urban landscape indices as well as the spatial association of the indices with the climate changes and urbanization process. Wuhan, a key megacity in Central China was selected as the case study. To this end, we mapped the urban greenness through NDVI of the city from 2000 to 2018 using Landsat imagery. The dynamics of the urban green space indicated by two landscape indices, namely Percent of landscape (PLAND) and landscape shape index (LSI), was analyzed for the study region. Time-series analysis using Mann-Kendall and Sen’s slope were adopted to reveal the temporal changes of the landscape pattern and Pearson’s correlation analysis was performed to explain its association with the climate changes and urbanization process. Results indicated that urban greenness not only significantly decreased but also fragmented.

The effects of transportation infrastructure on urban carbon emissions

In-depth exploration of the spatial heterogeneity patterns of urban carbon emissions holds significant scientific importance for regional sustainable development. However, few scholars have examined the spatiotemporal characteristics of county-level carbon emissions in Inner Mongolia. This study focuses on the three major cities of Hohhot, Baotou, and Ordos in Inner Mongolia. By integrating NPP-VIIRS nighttime light data, the CLCD (China Land Cover Dataset) dataset, and statistical yearbooks, it quantifies county-level carbon emissions and establishes a spatiotemporal analysis framework of urban morphology–carbon emissions from 2013 to 2021. Six morphological indicators—Class Area (CA), Landscape Shape Index (LSI), Largest Patch Index (LPI), Patch Cohesion Index (COHESION), Patch Density (PD), and Interspersion Juxtaposition Index (IJI)—are employed to represent urban scale, complexity, centrality, compactness, fragmentation, and adjacency, respectively, and their impacts on regional carbon emissions are examined. Using a geographically and temporally weighted regression (GTWR) model, the results indicate the following: (1) from 2013 to 2021, The high-value areas of carbon emissions in the three cities show a clustered distribution centered on the urban districts. The total carbon emissions increased from 20,670 (104 t/CO2) to 37,788 (104 t/CO2). The overall spatial pattern exhibits a north-to-south increasing gradient, and most areas are projected to experience accelerated carbon emission growth in the future; (2) the global Moran’s I values were all greater than zero and passed the significance tests, indicating that carbon emissions exhibit clustering characteristics; (3) the GTWR analysis revealed significant spatiotemporal heterogeneity in influencing factors, with different cities exhibiting varying directions and strengths of influence at different development stages. The ranking of influencing factors by degree of impact is: CA > LSI > COHESION > LPI > IJI > PD. This study explores urban carbon emissions and their heterogeneity from both temporal and spatial dimensions, providing a novel, more detailed regional perspective for urban carbon emission analysis. The findings enrich research on carbon emissions in Inner Mongolia and offer theoretical support for regional carbon reduction strategies.

PDF HTML阅读 XML下载 导出引用 引用提醒 长三角城市群碳排放与城市用地增长及形态的关系 DOI: 10.5846/stxb201707101242 作者: 作者单位: 浙江大学公共管理学院土地科学与不动产研究所,浙江大学公共管理学院土地科学与不动产研究所,浙江大学公共管理学院土地科学与不动产研究所,浙江大学公共管理学院土地科学与不动产研究所,浙江大学环境与资源学院农业遥感与信息技术应用研究所 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金面上项目（41771244）；国家留学基金（201706320200）；中央高校基本科研业务费专项资金资助；浙江大学文科教师教学科研发展专项项目 Relationships between carbon emission, urban growth, and urban forms of urban agglomeration in the Yangtze River Delta Author: Affiliation: Institute of Land Science and Property, School of Public Affairs, Zhejiang University,,Institute of Land Science and Property, School of Public Affairs, Zhejiang University,, Fund Project: National Natural Science Foundation of China（Grant No.41771244）; China Scholarship Council (Grant No.201706320200); supported by “the Fundamental Research Funds for the Central Universities”; supported by “the Teaching and Research Development Funds for Humanities and Social Sciences of Zhejiang University” 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:城市是一种重要的碳源，城市扩张过程中的用地面积增长和空间特征变化均会影响城市碳排放。分析1995-2015年长三角城市群碳排放重心转移，查明碳排放和城市用地增长的脱钩状态时空变化，并通过构建面板数据模型探究城市形态对碳排放的影响，得出以下结论：（1）1995-2015年长三角城市群碳排放重心经历了西南向-西北向-东南向-西北向的转移过程，这种转移过程与其相应时期内部分城市的工业发展与产业结构调整有关；（2）1995-2015年，长三角城市群碳排放与城市用地增长的脱钩状态存在着显著的时空异质性。研究区由以扩张负脱钩为主变化为以弱脱钩为主，2005年以后，区域之间的脱钩差异开始缩小，总体来看研究区脱钩状态趋向于同质。至2015年，近70%的城市已达到了脱钩，其中上海等城市实现了强脱钩；（3）连续完整的地块在区域内的主导程度会对城市碳排放产生负向的影响，而城市用地斑块的破碎化程度和聚集程度对碳排放有着正向的影响，且相对而言，聚集程度的正向影响更为显著。 Abstract:Cities are one of many carbon sources. According to the Intergovernmental Panel on Climate Change (IPCC) AR5, CO2 emissions from fossil fuel combustion and industrial processes contributed about 78% to the total Green House Gas (GHG) emission increase between 1970-2010. Total annual anthropogenic GHG emissions have increased by about 10GtCO2-eq between 2000-2010. The increase directly came from energy (47%), industry (30%), transport (11%), and building (3%) sectors, which mainly exist in cities. Urban expansion and urbanization can affect urban carbon emission. Studies show that there is a long-term and stable relationship between urbanization and carbon dioxide emissions. The relationships between urban carbon emissions and indicators, including urban development intensity, urban land use, and the industrial sector, are studied extensively. During urban expansion, the quantitative and spatial features of urban lands can both affect carbon emissions. Therefore, urban form was added to the possible factors influencing carbon emissions in this study, which may be different from previous research that has focused on the relationship between urban growth and carbon emissions. However, in some related research, when urban form has been added to the indicators, the objects were residents or the transport sector, and they lacked quantitative indicators to verify the conclusions. The definition of "urban form" in this study was landscape pattern which was characterized by landscape metrics, and the study area consisted of 13 cities in the Yangtze River Delta. In this study, we analysed the shift of the gravity center from 1995-2015 for carbon emissions of the study area, and defined the decoupling index as well as analysing the temporal-spatial changes of the decoupling relationships between carbon emissions and urban growth in the study area. We also built panel data models to estimate the impact of urban forms on carbon emissions. Based on that, the conclusions are as follows:(1) The shift of the gravity center from 1995-2015 for carbon emissions of the study area was southwest-northwest-southeast-northwest. The shift may be related to the development of industry and change of industrial structure in some cities during this period. (2) There was a significant temporal-spatial heterogeneity in the decoupling relationships between carbon emissions and urban growth from 1995-2015. The leading decoupling relationship between carbon emissions and urban growth of the study area changed from expansive negative decoupling to weak decoupling from 1995-2015. The difference of decoupling relationships between cities narrowed after 2005 and the overall decoupling relationship of the study area became homogeneous. In 2015, almost 70% of cities reached the decoupling state and the decoupling states of Shanghai, Shaoxing, and Taizhou were strong. (3) Urban carbon emissions can be negatively influenced by the dominance of complete patches, and positively influenced by the degree of fragmentation and aggregation of urban patches. Carbon emissions can be more sensitive to the more aggregative distribution of the urban patches. This study analysed the relationship between carbon emissions and urban growth, as well as exploring how urban form can affect carbon emissions. The conclusions could provide scientific references for the policy making of low-carbon development strategies and land use and urban planning of urban agglomeration in the Yangtze River Delta. 参考文献 相似文献 引证文献