基于NPP-VIIRS夜间灯光的长株潭地区县域土地利用碳排放空间分异研究

Spatial variation of carbon emissions from county land use in Chang-Zhu-Tan area based on NPP-VIIRS night light

从县域尺度开展土地利用碳排放空间分异研究,对指导区域实现碳达峰及碳中和目标具有重要意义.基于2013-2019年NPP-VIIRS夜间灯光数据测算长株潭地区县域土地利用碳排放,并运用探索性空间数据分析和地理探测器,对长株潭地区县域土地利用碳排放空间分异特征及影响因素进行分析.结果显示:(1)2013-2019年,长株潭地区县域土地利用碳排放差异较为显著,基本上在波动中呈递减趋势,空间上呈现自中部地区向四周地区渐弱的态势;(2)2013-2019年,研究区土地利用碳排放Moran′s I值呈"上升下降再上升"的趋势,由2013年的0.275841最终减少为2019年的0.270621,空间集聚特征较为显著,总体呈正相关;局部空间自相关均未出现高低、低低集聚型区域,且高高集聚型区域固定为湘潭市市辖区;(3)综合4年指标探测结果来看,长株潭地区县域土地利用碳排放影响因素的解释力q值为0.200~0.868.其中,单位能源消耗碳排放、第一产业产值比重、人均土地面积、土地利用程度指数、城镇化率在研究期间出现频率较高且出现时的q值均大于0.50,且任意两个影响因素交互作用后影响力表现为双因子增强和非线性增强.

The spatial variation of carbon emissions from county land use is essential to guide the region to achieve carbon peak and carbon neutrality. Based on the NPP-VIIRS night light data during 2013-2019 years, carbon emissions from county land use in Chang-Zhu-Xiang area were calculated. Meanwhile, the spatial characteristics and key factors of carbon emissions from county land use in Chang-Zhu-Xiang area were analyzed using exploratory spatial data analysis and geographic detector. The results showed that carbon emissions from county land use in Chang-Zhu-Xiang area has basically been reduced, and presented a decreasing trend from the central region to the surrounding region from 2013 to 2019. In addition, the Moran′s I value of carbon emissions from county land use presented fluctuated trend, which ranged from 0.275841 to 0.270621 during 2013-2019 years. The data analysis showed that the spatial difference of carbon emissions from land use was significant correlated with the distance. The Local Moran′s I analysis showed that there were no high-low and low-low agglomeration regions in Chang-Zhu-Xiang area, and the high-high agglomeration regions mainly located in Xiangtan city. The value of key factors influencing carbon emissions from county land use in Chang-Zhu-Xiang area was in range of 0.200 to 0.868. It was found that the carbon emissions per unit of energy consumption, the proportion of the output value of primary industry, the land area per capita, the index of land use degree, and the urbanization rate were the key factors. The influence on the carbon emissions from land use from multiple factors were much greater than the single factors.