Study on Spatiotemporal Pattern Evolution and Regional Heterogeneity of Carbon Emissions at the County Scale of Major Cities, Inner Mongolia Autonomous Region

Urban forests play a crucial role in enhancing vegetation cover and bolstering the ecological functions of cities by expanding green space, improving ecological connectivity, and reducing landscape fragmentation. This study examines these dynamics in Jinhua City, China, utilizing Landsat 8 satellite imagery for all four seasons of 2023, accessed through the Google Earth Engine (GEE) platform. Fractional vegetation cover (FVC) was calculated using the pixel binary model, followed by the classification of FVC levels. To understand the influence of landscape structure, nine representative landscape metrics were selected to construct a landscape index system. Pearson correlation analysis was employed to explore the relationships between these indices and seasonal FVC variations. Furthermore, the contribution of each index to seasonal FVC was quantified using a random forest (RF) regression model. The results indicate that (1) Jinhua exhibits the highest average FVC during the summer, reaching 0.67, while the lowest value is observed in winter, at 0.49. The proportion of areas with very high coverage peaks in summer, accounting for 50.6% of the total area; (2) all landscape metrics exhibited significant correlations with seasonal FVC. Among them, the class area (CA), percentage of landscape (PLAND), largest patch index (LPI), and patch cohesion index (COHESION) showed strong positive correlations with FVC, whereas the total edge length (TE), landscape shape index (LSI), patch density (PD), edge density (ED), and area-weighted mean shape index (AWMSI) were negatively correlated with FVC; (3) RF regression analysis revealed that CA and PLAND contributed most substantially to FVC, followed by COHESION and LPI, while PD, AWMSI, LSI, TE, and ED demonstrated relatively lower contributions. These findings provide valuable insights for optimizing urban forest landscape design and enhancing urban vegetation cover, underscoring that increasing large, interconnected forest patches represents an effective strategy for improving FVC in urban environments.

Urban morphology’s effects on carbon dioxide reduction and sustainable development have drawn more attention. The county scale is crucial in influencing urban development and is the central element of China’s recent urbanization. To achieve scientific urban planning and fully explore its potential in carbon emission reduction, local governments need to investigate the impact of urban morphology on carbon emissions (CE). However, previous studies have predominantly focused on provincial capitals and urban clusters. To address this gap, this study quantified four aspects of urban form, combined energy consumption, and nighttime light data to estimate CE in Chinese counties from 2000 to 2020 and analyzed the effects of these factors on CE using multiscale geographically weighted Regression(MGWR) models and geographic detectors. The following are the main findings: (1) Total CE at the county scale in China has consistently increased from 2000 to 2020. (2) The largest patch index (LPI) is the most influential urban morphological factor on CE, while the impact of Class Area (CA) has been increasing. (3) Bi-factor enhancement and nonlinear enhancement are the two primary interaction types of urban morphological factors; the most important interaction is between LSI and CA. (4) The urban morphological factors exhibit varying degrees of spatial heterogeneity, with the influencing factors ranked as CA > LPI > path density (PD) > edge density (ED) > patch cohesion index (COHESION), where LPI and CA consistently show a positive effect on CE. This study’s findings establish a scientific foundation for land spatial planning and tailored emission reduction methods at the county scale in China.

In order to study the influence of urban green space landscape pattern on urban carbon emissions， nighttime lighting data， socioeconomic development data， and land use remote sensing data from 2000 to 2020 are used as the basis of analysis， and the three major coastal economically developed regions in China-Bohai Rim， Yangtze River Delta （YRD）， and Pearl River Delta （PRD） （nearly 100 cities in total） are used as the study area to analyze the spatial and temporal evolution characteristics of urban carbon emissions， as well as the influence of urban green space landscape pattern and its spatial and temporal changes. We also explored the influence of 10 urban green space landscape pattern indices on urban carbon emissions by using the random forest model and the Lasso regression model and further analyzed the four factors （number of patches， density of patches， dispersion of patches， and complexity of the shape of patches） that had a greater influence by using the spatio-temporal geographically weighted regression model， to explore the results of the spatial and temporal evolution of the influence of the urban green space landscape pattern on carbon emissions. The main findings of this study are as follows： ① Carbon emissions in the three study areas showed a slow growth trend， with the Bohai Rim showing a relatively fast growth rate. Carbon emissions were spatially aggregated in the selected study areas， with the majority of cities in the "high and high" agglomeration and the "low and low" agglomeration regions. There was spatial aggregation of carbon emissions in the selected study areas， with the majority of cities in "high and high" agglomeration and "low and low" agglomeration. The land-averaged carbon emissions in the three study areas were dispersed in all directions， with the economically strong cities as the core， and the overall carbon emission level was dispersed from the center to the surroundings. Additionally， along the rivers and coastal areas， carbon emissions were higher due to the concentration of ports， industrial zones， and cities. ② Landscape occupied by patches， number of patches， and density of patches had a significant negative correlation with urban carbon emissions， which indicates that the higher the number， density， and proportion of the landscape occupied by urban green space patches， the more it could hinder the growth of carbon emissions. On the contrary， the shape index and patch fragmentation index had a positive correlation with urban carbon emissions， indicating that the higher the shape complexity of urban green space patches and the higher the fragmentation degree of patches， the more it promoted the growth of urban carbon emissions. In addition， the aggregation index also showed a significant negative correlation with urban carbon emissions， which indicates that the higher the degree of aggregation of patches， the more it could inhibit the growth of carbon emissions. ③ The correlation between the green space landscape pattern index and carbon emissions showed significant spatial and temporal differences， with large changes around 2010. In the Bohai Rim Region， the influence of the urban landscape pattern index on carbon emissions remained relatively stable， and its influence over time generally showed a decline. In the YRD Region， the shape complexity and dispersion of urban green space had a greater impact on carbon emissions than the number of patches and patch density factors. However， on the contrary， in the PRD Region， the impacts of the number of urban green spaces and density index were increasing. In addition， the spatial influence changes on all showed the clustering of regression coefficients. The impact of urban green space on carbon emissions varied greatly across locations and time， suggesting that policy makers cannot rely on a one-size-fits-all approach to urban green space planning. In the Bohai Rim Region， it is more important to balance the distribution of urban green space with other land uses to maintain stability； in the YRD Region， highly fragmented and overly complex green space patch planning should be reduced； and in the PRD Region， priority should be given to increasing the amount and distribution density of urban green space.

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.

Analyzing the relationship between three-dimensional architectural landscapes and urban carbon emissions using machine learning approaches

PDF HTML阅读 XML下载 导出引用 引用提醒 三峡库区典型流域“源”“汇”景观格局时空变化对侵蚀产沙的影响 DOI: 10.5846/stxb201809252079 作者: 作者单位: 华中农业大学,华中农业大学,华中农业大学,华中农业大学,华中农业大学,华中农业大学 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（41571266，41877071）；中央高校基本科研业务费专项资助项目（2662016QD030） Effects of temporal and spatial variations in source-sink landscape patterns on soil erosion and sediment yield from typical watershed in the Three Gorges Reservoir area, China Author: Affiliation: Huazhong Agricultural University,,Huazhong Agricultural University,Huazhong Agricultural University,Huazhong Agricultural University, Fund Project: The National Natural Science Foundation of China [grant numbers 41571266], the National Natural Science Foundation of China [grant numbers 41877071], the Fundamental Research Funds for the Central Universities Project of China [grant numbers 2662016QD030]. 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:以三峡库区王家桥流域为研究对象，基于五期遥感影像（1995、2000、2005、2010和2015）提取流域景观格局，采用马尔科夫模型研究"源""汇"景观格局时空变化，并结合野外多年实测数据建立景观格局指数与产沙量之间的关系，分析景观格局对流域侵蚀产沙的影响。结果表明：1995-2015年，王家桥流域景观格局时空变化明显，呈破碎化发展趋势。"源"景观面积持续下降，"汇"景观面积持续增长。在降雨量时序曲线相似的条件下，斑块类型尺度的景观指数对侵蚀产沙量的解释能力高于景观尺度。斑块类型尺度， "源" "汇"景观指数与径流输沙量的复相关系数分别为0.946和0.903，均显著相关。对于"源"景观而言，相似邻近百分比（PLADJ）与斑块结合度（COHESION）是影响流域侵蚀产沙的重要指标。径流输沙量与PLADJ和COHESION指数呈正相关关系，输沙量随指数的增大而增大。对于"汇"景观而言，斑块密度（PD）、边界密度（ED）、景观形状指数（LSI）是影响流域侵蚀产沙的重要指标，径流输沙量与PD和LSI呈负相关关系，输沙量随着指数的增大而减小。斑块类型尺度上，流域景观格局时空变化对泥沙输出影响显著。研究结果可为建立其他流域景观格局变化与土壤侵蚀响应关系提供参考。 Abstract:Taking the Wangjiaqiao watershed as the study region, the landscape patterns of the watershed were extracted based on remote sensing images over five periods (1995, 2000, 2005, 2010, and 2015). The temporal-spatial evolution characteristic of the source-sink landscape patterns were analyzed using transfer matrixes with the Markov model. In addition, the relationship between landscape pattern indexes and sediment yield were established based on field measured data of the Wangjiaqiao watershed for many years, and the influence of landscape patterns on the sediment yield of the watershed was analyzed. The results concluded that temporal and spatial variations in landscape patterns were obvious in the Wangjiaqiao watershed from 1995 to 2015, showing a fragmentation trend of development. The area of source landscape continued to decline, while the area of sink landscape continued to increase. Under the condition that the time-series curves of precipitation are similar, the landscape index at patch scale had a higher interpretation ability to erosion sediment yield than at landscape scale. At patch scale, the multiple correlation coefficients between sediment yield and landscape indexes of source and sink were 0.946 and 0.903 respectively, showing a significant correlation. The Proportion of Like Adjacency (PLADJ) and Patch Cohesion index (COHESION) were important landscape indexes affecting sediment yield in the watershed for the source landscape. Both PLADJ and COHESION were positively correlated with the sediment yield, and the sediment yield increased with the increasing value of the above two indexes. As for the sink landscape, Patch Density (PD), Edge Density (ED), and the Landscape Shape Index (LSI) were important indexes of soil erosion and sediment yield. Both PD and LSI were negatively correlated with sediment yield, and the sediment yield decreased with an increasing value of the two indexes. At patch scale, the influence of temporal and spatial variation of landscape patterns on sediment yield was significant. These results could provide references for establishing the relationship between variations in landscape patterns and responses of soil erosion to other watersheds. 参考文献 相似文献 引证文献

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.

Impervious surfaces (IPS) are the major source of urban heat island effect (UHI), and the relationships between IPS and land surface temperature (LST) have been widely studied. However, the spatial impact of landscape patterns of patches with different IPS density (IPSD) on the thermal environment remains largely unexplored. Based on three Landsat 8 images of the Xuzhou built-up area obtained in May and the corresponding ground observations from 2014 to 2020, the IPSD and LST maps were inversed through a linear spectral mixture analysis and mono-window algorithm, respectively. The landscape patterns of the five IPSD levels were characterized by four landscape-level and five class-level metrics. Finally, the spatial correlation between all landscape metrics and LST were analyzed using bivariate Moran’s I. The results were as follows: (1) The findings revealed that for the landscape-level metrics, LST had significant positive spatial correlations with Shannon’s diversity index (SHDI), Shannon’s evenness index (SHEI), and patch density (PD), while showing a significant negative correlation with contagion index (CONTAG), indicating that increasing the types, even distribution degree, and density of patches, or decreasing the aggregation degree of the five IPSD levels will lead to the enhancement of the thermal environment. (2) Furthermore, the class-level metrics of each IPSD level, percentage of landscape (PLAND), largest patch index (LPI), landscape shape index (LSI), aggregation index (AI), and patch cohesion index (COHESION) showed significant correlations and LST, which signified that the spatial characteristics of patch proportion, predominance degree, shape complexity, aggregation degree, and natural connectivity degree of each IPSD level are important factors affecting UHI. In addition, the spatial correlations between LST and class-level metrics were significantly positive for IPSD levels 4 and 5 with an evidently higher Moran’s I value, indicating that landscape patterns of IPSD levels 4 and 5 were the key factors in UHI enhancement. Furthermore, the impact weights of each class-level metric of IPSD levels 4 and 5 on LST were also analyzed by applying the principal component analysis and the multivariate regression standardization coefficient. These results reveal the importance and impact mechanism of the IPSD spatial patterns on UHI evolution, which may provide a valuable reference for future urban planning and climate management. This study also suggests that regional UHI can be mitigated by reducing the area proportion, natural connectivity, and shape complexity of high-density impervious surfaces.

Climate warming caused by carbon emissions is a hot topic in the international community. Research on urban industrial carbon emissions in China is of great significance for promoting the low-carbon transformation and spatial layout optimization of Chinese industry. Based on ArcGIS spatial analysis, Markov matrix and other methods, this paper calculates and analyzes the temporal and spatial evolution characteristics of industrial carbon emissions in 282 cities in China from 2003 to 2016. Based on the spatial Dubin model, the influencing factors of urban industrial carbon emissions in China and different regions are systematically analyzed. The study shows that (1) China’s urban industrial carbon emissions generally show a trend of first growth and then slow decline. The trend of urban industrial carbon emissions in the western, central, northeastern and eastern regions of China is basically consistent with the overall national trend; (2) In 2003, China’s urban industrial carbon emissions were dominated by low carbon emissions. In 2016, China’s urban industrial carbon emissions were dominated by high carbon emissions, and the spatial trend is gradually decreasing from the eastern region to the central region to the northeast region to the western region; (3) In 2003, the evolution pattern of China’s urban industrial carbon emissions was “low carbon-horizontal expansion” dominated by positive growth, and in 2016, it was “low carbon-vertical expansion” dominated by scale growth; (4) China’s urban industrial carbon emissions have spatial viscosity, and the spatial viscosity decreases with the increase of industrial carbon emissions. (5) In 2004, the relationship between urban industrial carbon emissions and gross industrial output value in China is mainly weak decoupling. In 2016, various types of decoupling regions are more diversified and dispersed, and strong decoupling cities are mainly formed from weak decoupling cities in southwest China and eastern coastal areas; (6) From a national perspective, indicators that are significantly positively correlated with industrial carbon emissions are urban industrial structure, industrial agglomeration level, industrial enterprise scale and urban economic development level, in descending order. Indicators that are significantly negatively correlated with urban industrial carbon emissions are industrial structure and industrial ownership structure, in descending order. Due to the different stages of industrial development and industrial structure in different regions, the influencing factors are also different.

The process of soil erosion as the central focus of soil and water conservation studies in sustainable ecosystems is influenced by various natural and human disturbances. Besides, the overall structure and composition of an ecosystem are affected by changes in its landscape. Therefore, the present study was planned to investigate the relationship between landscape metrics and soil erosion patterns in the KoozehTopraghi Watershed located in Ardabil Province. In this regard, the 14 landscape metrics including Patch Density (PD), Largest Patch Index (LPI), Total Edge (TE), Edge Density (ED), Landscape Shape Index (LSI), Mean Patch Area (AREA-MN), Mean Euclidean Nearest Neighbor Distance (END-MN), Landscape Division Index (DIVISION), Mean Patch Shape Index (SHAPE-MN), Splitting Index (SPLIT), Patch Cohesion Index (COHESION ), Effective Mesh Size (MESH), Aggregation Index (AI) and Percentage of Landscape (PLAND) were calculated using Fragstats 4.2.1 Software. Then, the severity of soil erosion in the study area was estimated using the Erosion Potential Method (EPM), which its performance has been confirmed by previous studies around the study area. Then, the results of landscape metrics and soil erosion calculation were interred to IBM SPSS Statistics software platform, and the established regression models were determined. The results indicate an inverse relationship between DIVISION, ED, and LPI landscape metrics with the specific soil erosion and a direct relationship between SHAPE-MN and AREA-MN with specific soil erosion of the KoozehTopraghi Watershed. The results of the present study confirm the different behavioral patterns of land features as a result of land degradation processes. These results can be used in effective and sound planning of land use management at watershed and landscape scales.

The urban heat island (UHI) phenomenon is negatively impacted by rapid urbanization, which significantly affects people's everyday lives, socioeconomic activities, and the urban thermal environment. This study focuses on the impact of composition, configuration, and landscape patterns on land surface temperature (LST) in Lahore, Pakistan. The study uses Landsat 5-TM and Landsat 8-OLI/TIRS data acquired over the years 2000, 2010 and 2020 to derive detailed information on land use, normalized difference vegetation index, LST, urban cooling islands (UCI), green cooling islands (GCI) and landscape metrics at the class and landscape level such as percentage of the landscape (PLAND), patch density (PD), class area (CA), largest patch index (LPI), number of patches (NP), aggregation index (AI), Landscape Shape Index (LSI), patch richness (PR), and mean patch shape index (SHAPE_MN). The study's results show that from the years 2000 to 2020, the built-up area increased by 17.57%, whereas vacant land, vegetation, and water bodies declined by 03.79%, 13.32% and 0.4% respectively. Furthermore, landscape metrics at the class level (PLAND, LSI, LPI, PD, AI, and NP) show that the landscape of Lahore is becoming increasingly heterogeneous and fragmented over time. The mean LST in the study area exhibited an increasing trend i.e. 18.87°C in 2000, 20.93°C in 2010, and 22.54°C in 2020. The significant contribution of green spaces is vital for reducing the effects of UHI and is highlighted by the fact that the mean LST of impervious surfaces is, on average, roughly 3°C higher than that of urban green spaces. The findings also demonstrate that there is a strong correlation between mean LST and both the amount of green space (which is negative) and impermeable surface (which is positive). The increasing trend of fragmentation and shape complexity highlighted a positive correlation with LST, while all area-related matrices including PLAND, CA and LPI displayed a negative correlation with LST. The mean LST was significantly correlated with the size, complexity of the shape, and aggregation of the patches of impervious surface and green space, although aggregation demonstrated the most constant and robust correlation. The results indicate that to create healthier and more comfortable environments in cities, the configuration and composition of urban impermeable surfaces and green spaces should be important considerations during the landscape planning and urban design processes.

Landscape indices can quantitatively describe the distribution characteristics of biological soil crusts (biocrusts). However, there are too many landscape indices, with high redundancy. We investigated 58 plots of biocrusts with different distribution patterns in the Hegou watershed of Wuqi County, Shaanxi Province, located in the hilly Loess Plateau. First, we calculated 15 common landscape indices, and selected representative landscape indices that could describe the biocrust landscape pattern and had specific ecological significance, based on correlation analysis, factor analysis, and sensitivity analysis. The reliability and rationality of the representative landscape indices were verified with data of the different biocrusts coverage in the Yingwoshanjian watershed of Yangjing Town, Dingbian County, Shaanxi Province. The results showed that 10 of the 15 landscape indices had significant correlations. Total edge (TE) and edge density (ED) were not significantly correlated with number of patches (NP), patch density (PD), clumpiness (CLUMPY), and interspersion juxtaposition index (IJI), respectively. The percentage of landscape (PLAND), ED, patch cohesion index (COHESION), and splitting index (SPLIT) described the spatial distribution characteristics of biocrust from coverage, length, connectivity, and fragmentation, respectively. The cumulative contribution of the three common factors represented in describing the spatial distribution of biocrusts was 91.6%. The study identified the representative landscape indices that could quantify the complexity of biocrusts distribution and thus would provide a theoretical basis for studying the pattern evolution of biocrusts and their relationship with ecological processes.

Impact of land-use/land-cover and landscape pattern on seasonal in-stream water quality in small watersheds

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.

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