Zhang Yangyang, Hong Wenxia
School of Management Engineering, Qingdao University of Technology, Qingdao, 266520, China.
Environ Sci Pollut Res Int. 2024 Feb;31(9):13897-13924. doi: 10.1007/s11356-024-32057-y. Epub 2024 Jan 24.
The total change in carbon emissions in the Bohai Rim Region (BRR) plays a guiding role in the policy formulation of carbon emission reduction in northern China. Taking the 43 cities in the BRR as an example, the spatial-temporal evolution of carbon emissions in the BRR was analyzed using kernel density estimation (KDE), map visualization, and standard deviation ellipses, and the spatial autocorrelation model was used to explore the spatial clustering of carbon emissions. On this basis, the spatial-temporal heterogeneity of the factors influencing carbon emissions is explained using a Geodetector. The results are as follows: (i) During the study period, the carbon emissions in the BRR were on the rise, the share of carbon emissions in the Beijing-Tianjin-Hebei region (BTHR) and Liaoning Province was decreasing, and the contribution of Shandong Province was gradually enhanced. The spatial distribution of carbon emissions shows a geographical pattern of "middle-high and low-outside." (ii) Carbon emissions from different regions show the characteristics of BTHR > Shandong Province > Liaoning Province. The high-value carbon emission area continues to move from the northwest of Beijing-Tianjin-Hebei to the southeast. (iii) Municipal carbon emissions showed a significant positive spatial correlation in the later part of the study. The high-high aggregation area is in Tianjin, and the low-low aggregation area is in Liaoning Province. (iv) The level of transport development contributes to carbon emissions with the highest growth rate, followed by industrial structure. There are also regional differences in the dominant influences on municipal carbon emission differences. Population size, urbanization, and economic development level are the core influencing factors of carbon emissions in the BTHR, Shandong Province, and Liaoning Province, respectively. In addition, the explanatory power of the interaction between the level of economic development and other factors on carbon emissions is at a high level.
环渤海地区碳排放总量变化对中国北方碳排放减排政策制定具有指导作用。以环渤海地区43个城市为例,运用核密度估计(KDE)、地图可视化和标准差椭圆分析了环渤海地区碳排放的时空演变,并采用空间自相关模型探讨了碳排放的空间集聚性。在此基础上,利用地理探测器解释影响碳排放因素的时空异质性。结果如下:(i)研究期内,环渤海地区碳排放呈上升趋势,京津冀地区(BTHR)和辽宁省碳排放占比下降,山东省贡献逐渐增强。碳排放空间分布呈现“中间高、周边低”的地理格局。(ii)不同地区碳排放呈现BTHR>山东省>辽宁省的特征。高值碳排放区持续从京津冀西北部向东南部移动。(iii)研究后期,城市碳排放呈现显著正空间相关性。高高集聚区在天津,低低集聚区在辽宁省。(iv)交通运输发展水平对碳排放贡献率增长最高,其次是产业结构。影响城市碳排放差异的主导因素也存在区域差异。人口规模、城市化和经济发展水平分别是京津冀地区、山东省和辽宁省碳排放的核心影响因素。此外,经济发展水平与其他因素交互作用对碳排放的解释力处于较高水平。