Multi-scale spatial relationship between carbon emissions and influencing factors in the Yangtze River Delta
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摘要:
在推进生态文明建设的新时代背景下，探究碳排放的分布规律、了解碳排放的驱动因素，对因地制宜制定减排政策、促进区域高质量发展具有十分重要的意义。长三角城市群是中国经济发展最活跃的区域之一，同时也面临着日益严峻的碳排放问题。以长三角城市群碳排放量为研究对象，以县域为研究尺度，应用莫兰指数、冷热点分析等空间分析方法，挖掘该区域碳排放的空间分布规律，并基于MGWR模型分析该区域碳排放及其影响因素的多尺度空间关系。结果表明：①长三角城市群碳排放在空间上存在显著的H-H（高−高）型集聚和L-L（低−低）型集聚；②长三角城市群碳排放的冷点主要分布在安徽省宣城市和安庆市，热点主要分布在上海市及苏南地区；③GPP、道路密度、GDP、产业占比等在全局范围内对碳排放产生不同影响，NDVI、人口密度、用电量在局部范围内对碳排放产生不同影响。道路密度、GDP、用电量越大对碳排放的正向影响越大，第三产业占比越小对碳排放的负面影响越大。提出通过优化交通路线、鼓励绿色出行、加强道路监管等减少高密度交通网带来的碳排放，通过优化城镇产业布局、升级产业结构、加快技术升级等减少因产业结构带来的影响，通过引导劳动力合理转移、扩大城市绿化面积、提升区域固态能力等减少碳排放。
Abstract:In the context of the new era of promoting the construction of ecological civilization, it is of great significance to explore the distribution pattern of carbon emissions and understand the driving factors of carbon emissions, in order to formulate emission reduction policies in accordance with local conditions and promote the high−quality development of the region. The Yangtze River Delta urban agglomeration is one of the most active regions in China's economic development, and at the same time, it is also facing the increasingly serious problem of carbon emissions. Taking the carbon emissions of the Yangtze River Delta urban agglomeration as the research object and the county as the research scale, we apply the spatial analysis methods such as Moran's I, cold and hot spot analysis to excavate the spatial distribution of carbon emissions in the region and analyze the multi−scale spatial relationship between carbon emissions and their influencing factors in the region based on the MGWR model. The following results are drawn: ① There are significant H−H (high−high) clustering and L−L (low−low) clustering of carbon emissions in the Yangtze River Delta urban agglomeration; ② The cold spots of carbon emissions in the Yangtze River Delta urban agglomeration are mainly distributed in Xuancheng and Anqing cities in Anhui Province, while the hot spots are mainly distributed in Shanghai, as well as in the southern part of Jiangsu Province; ③ GPP, road density, GDP, the proportion of the primary, secondary and tertiary industry have different impacts on carbon emissions on the global scale, and NDVI, population density and electricity consumption have different effects on carbon emission in local scale. It is also concluded that the greater the road density, GDP and electricity consumption, the greater the positive impact on carbon emissions, and the smaller the proportion of tertiary industry, the greater the negative impact on carbon emissions. The paper also puts forward several policy suggestions to reduce carbon emissions in the Yangtze River Delta urban agglomeration, including reducing carbon emissions due to high−density transportation network by optimizing transportation routes,encouraging green travel, and strengthening road supervision, etc., reducing the impact due to industrial structure by optimizing the industrial layout of towns and cities, upgrading the industrial structure and accelerating the technological upgrading, and reducing carbon emissions by guiding the reasonable transfer of labor force, expanding the greening area of the cities and upgrading the region's solid state capacity, etc. to reduce carbon emissions.
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表 1 解释变量描述
Table 1. Explanatory variable description
变量类型 变量 变量描述 单位 空间分辨率 时间分辨率 自变量 碳排放量 化石燃料燃烧、生产水泥和天然气燃烧
等产生的二氧化碳排量t/km2 1 km 年度 自然因子解释变量 归一化
植被指数县域尺度平均
归一化植被指数1 km 年度 总初级
生产力县域尺度平均
总初生产力kgC/m² 500 km 年度 社会经济解释变量 道路密度 城市道路占地 /km 1 km 年度 人口密度 单位面积上的人口数量 /km 1 km 年度 用电量 县域尺度总用电量 kw·h 1 km 年度 GDP 县域尺度国内生产总值 元 县域 年度 第一产业占比 县域尺度第一产业占比 % 县域 年度 第一产业占比 县域尺度第二产业占比 % 县域 年度 第一产业占比 县域尺度第三产业占比 % 县域 年度 
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表 2 基于MGWR模型的解释变量回归结果
Table 2. The regression results of explanatory variables based on MGWR Model
解释 MGWR 变量 带宽 均值 标准差 最小值 最大值 Intercept 201.000 7.303 0.019 7.268 7.339 NDVI 45.000 −0.321 0.328 −0.962 0.290 GPP 201.000 −0.016 0.020 −0.054 0.022 道路密度 201.000 0.145 0.010 0.123 0.162 人口密度 129.000 −0.057 0.174 −0.315 0.281 GDP 201.000 0.097 0.007 0.088 0.114 用电量 80.000 0.696 0.170 0.292 0.945 第一产业占比 201.000 −0.051 0.020 −0.551 −0.458 第二产业占比 201.000 −0.012 0.008 −0.023 0.013 第三产业占比 201.000 −0.088 0.008 −0.100 −0.073
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