Monitoring spatiotemporal characteristics of land-use carbon emissions and their driving mechanisms in the Yellow River Delta: A grid-scale analysis.
Monitoring spatiotemporal characteristics of land-use carbon emissions and their driving mechanisms in the Yellow River Delta: A grid-scale analysis.
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DOI：
10.1016/j.envres.2022.114151
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发表时间：
2022-08
影响因子：
8.3
通讯作者：
Yijia Yang;Huiying Li
中科院分区：
文献类型：
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作者：
Yijia Yang;Huiying Li
全面准确地把握土地利用碳排放水平及其驱动机制，是我国低碳发展取得成功的关键，也是制定和实施区域碳排放战略的科学依据。基于化石燃料碳排放栅格数据本文以黄河三角洲为研究区域，采用改进的LCE测量模型、探索性空间数据分析、多尺度地理加权回归、和其他模型，以探讨时空异质性和驱动机制的LCE在网格水平。结果表明：① 2000 - 2019年研究区LCE总量持续增加，增长速度有所下降，但尚未达到LCE峰值。②研究区LCE总体上呈现显著的正自相关。H-H聚集区空间分布范围相对稳定; L-L聚集区分布范围较广，基本覆盖整个研究区; H-L和L-H聚集区尚未达到尺度。③在全球维度上，LCE与驱动因子的平均相关系数2000年至2019年的净初级生产力（NPP），夜间光照（NTL）和人口密度（PD）分别为-0.11，0.28和0.12;在局部维度上，各因子对LCE的影响强度（由强到弱）依次为PD、NTL、NPP（2000）和NTL、PD、NPP（2019）。研究成果为区域碳排放战略的制定和实施提供了科学依据和基本保障。
Comprehensive and accurate grasp of land-use carbon emissions (LCE) level and its driving mechanism is key to success in China's pursuit of low-carbon development, and it is also the scientific basis for the formulation and implementation of regional carbon emissions strategies. Based on fossil fuel carbon emissions raster data (published by the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) platform) and land use data, this manuscript selects the Yellow River Delta as the study area and uses an improved LCE measurement model, exploratory spatial data analysis, multiscale geographical weighting regression (MGWR), and other models to explore the spatiotemporal heterogeneity and driving mechanisms of LCE at the grid level. The results showed the following: ① The total amount of LCE in the study area continued to increase from 2000 to 2019, the growth rate decreased, but the peak of LCE had not yet been reached. ② The LCE of the study area showed a significant positive global autocorrelation. The H–H aggregation region showed a relatively stable spatial distribution range; the L-L aggregation region showed wide distribution characteristics that covered the entire study area; and the aggregation regions of H-L and L-H, which have not yet reached the scale. ③ At the global dimension, the mean correlation coefficients between LCE and driving factors (net primary productivity (NPP), nighttime light (NTL), and population density (PD)) from 2000 to 2019 were −0.11, 0.28, and 0.12; at the local dimension, the strength (from strong to weak) of the effect of each factor on LCE was PD, NTL, NPP (2000) and NTL, PD, NPP (2019). The research results provide a scientific basis and basic guarantee for the development, and implementation of regional carbon emission strategies.