土地利用隐性形态对碳排放的影响研究以长江中游城市群为例

Influence of Land Use Recessive Morphology on Carbon Emission in the Middle Reaches of Yangtze River

  • 摘要:
    目的 探究土地利用隐性形态及其对碳排放的影响,为面向“双碳”目标的土地利用空间优化提供实践指导。
    方法 以长江中游城市群为研究区,基于土地利用数据,构建土地利用隐性形态指标体系,采用碳排放系数法和地理探测器模型,分析2010 ~ 2020年长江中游城市群土地利用隐性形态和土地利用碳排放时空变化特征,揭示土地利用隐性形态与碳排放的相互关系。
    结果 ①长江中游城市群土地利用隐性形态水平持续升高,并呈现以省会城市单核集聚的“中三角”向省会城市相连的“中三轴”空间格局演变。②建设用地是研究区最主要土地利用碳源,占碳排放量的96%以上。林地是碳汇的主要来源,占碳吸收能力的89%。土地利用净碳排放呈不断上升趋势,增长率达11.59%。高值区主要集中在武汉、南昌、荆门、宜昌、九江市等长江沿岸带以及萍乡、新余、湘潭市等老工业基地城市。③地理探测器结果显示,第二产业产值、第三产业产值、地均用电量以及土地城镇化率是影响土地利用净碳排放量的关键驱动因子,q值为0.6以上。
    结论 土地利用隐性形态向高阶转型促进了土地利用碳排放,净碳排放的空间分布是由多因子交互作用形成的,土地城镇化率对各指标层碳排放解释力最高。

     

    Abstract:
    Objective The purpose of this study was to explore land use recessive morphology and its impact on land use carbon emissions, to provide practical guidance for spatial optimization of land use based on the "dual carbon" goals.
    Method The Middle reaches of Yangtze River was taken as the research area, land use recessive form indicators were selected, spatial-temporal variation characteristics of land use recessive morphology and land use carbon emission were analyzed using carbon emission coefficient method and geographic detector model, based on land use data from 2010 to 2020, and revealed the correlations of land use recessive morphology and carbon emission.
    Result ① The level of land use recessive morphology in the Middle Reaches of Yangtze River continued to increase, and the spatial pattern evolved from the "Middle triangle" with single provincial capital to the "Middle three-axis" with connected provincial capital. ② Construction land was the most important carbon source in the study area, accounting for more than 96% of carbon emissions. Forest land was the main source of carbon sink, accounting for 89% of carbon absorption capacity, and the net carbon emission from land use showed a rising trend, with an annual growth rate of 11.56%. High value areas of carbon emissions were mainly concentrated in Wuhan, Nanchang, Jingmen, Yichang, Jiujiang cities along the Yangtze River and Pingxiang, Xinyu, Xiangtan cities in the old industrial base. ③Geographic detector showed that the output value of secondary industry, the output value of tertiary industry, the average power consumption of land and the urbanization rate of land were the key driving factors affecting the net carbon emissions of land use. The q value of driving factors was above 0.6.
    Conclusion The hidden recessive morphology of land use increased land use carbon emissions. The spatial distribution of net carbon emissions was formed by the interaction of multiple factors, and the urbanization rate of land had the highest explanatory power for carbon emissions in each index layer.

     

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