| 兰西城市群能源消费碳排放时空分布特征及影响因素 |
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| 引用本文： | 邓光耀,陈刚刚. 兰西城市群能源消费碳排放时空分布特征及影响因素[J]. 西华大学学报(哲学社会科学版), 2022, 41(6): 40-60. DOI: 10.12189/j.issn.1672-8505.2022.06.005 |
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| 作者姓名： | 邓光耀 陈刚刚 |
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| 作者单位： | 1.兰州财经大学甘肃经济发展数量分析研究中心 甘肃兰州 730020 |
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| 摘 要： | 在全国碳减排目标框架下，从县域尺度开展能源消费碳排放时空演变及其影响因素的研究，对指导城市群实现碳达峰及碳中和目标具有重要意义。探索性空间数据分析可以揭示区域内碳排放的空间差异性，而地理探测器不仅可以识别影响因素对碳排放单独的影响力，还可以识别影响因素交互效应对碳排放空间分异的影响力。因此，文章通过校正融合长时间序列DMSP/OLS与NPP/VIIRS夜间灯光影像，空间化模拟了1995—2019年兰西城市群能源消费碳排放量，并从县域尺度视角出发，利用探索性空间数据分析以及地理探测器等，对碳排放时空分布特征、空间关联特征及影响因素等展开研究。结果表明:（1）碳排放总量从1995年的36.23×106 t上升到2019年的116.61×106 t，增长速度呈先上升后下降的态势，年均增速为4.79%，县域碳排放（104t）区间由1995年的[13.4, 425.4]增长为2019年的[103.2, 1051.4]；（2）高碳县区主要集中在兰州市和西宁市主城区以及周围人口密集、经济较发达的县区，空间差异性在不断缩小，空间正自相关性呈现出逐步扩大的趋势，局部自相关比较稳定，以高—高聚集和低—低聚集为主导；（3）碳排放空间分异受多种因素综合作用，经济发展水平对碳排放空间分异的影响力始终最强。
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| 关 键 词： | 能源消费碳排放 夜间灯光影像 时空分布特征 地理探测器 兰西城市群 |
| 收稿时间： | 2022-08-10 |
Spatio-temporal Evolution Pattern and Influencing Factors of Carbon Emissions from Energy Consumption in Lanzhou-Xining City Group-Based on DMSP/OLS and NPP/VIIRS Night Light Data |
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| DENG Guang-yao,CHEN Gang-gang. Spatio-temporal Evolution Pattern and Influencing Factors of Carbon Emissions from Energy Consumption in Lanzhou-Xining City Group-Based on DMSP/OLS and NPP/VIIRS Night Light Data[J]. Journal of Xihua University:Philosophy & Social Sciences, 2022, 41(6): 40-60. DOI: 10.12189/j.issn.1672-8505.2022.06.005 |
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| Authors: | DENG Guang-yao CHEN Gang-gang |
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| Affiliation: | 1.Center for Quantitative Analysis of Gansu Economic Development, Lanzhou University of Finance and Economics, Lanzhou, Gansu, 730020, China |
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| Abstract: | Under the framework of the national carbon emission reduction goals, the research on the spatio-temporal evolution of energy consumption carbon emissions and its influencing factors at the county level is of great significance to guide urban agglomerations to achieve carbon peak and carbon neutral goals. Exploratory spatial data analysis can reveal the spatial differences of carbon emissions in the region, while geographic detectors can not only identify the independent influence of influencing factors on carbon emissions, but also identify the influence of interaction effects of influencing factors on the spatial differentiation of carbon emissions. Therefore, the paper spatially simulates the carbon emissions of energy consumption in Lan-Xi urban agglomeration from 1995 to 2019 by correcting and fusing the long time series DMSP/OLS and NPP/VIIRS night light images. From the perspective of county scale, the paper uses exploratory spatial data analysis and geographical detectors to study the spatio-temporal distribution characteristics, spatial correlation characteristics and influencing factors of carbon emissions. The results show that: (1) The total carbon emissions increased from 36.23×106 t in 1995 to 116.61×106 t in 2019, and the growth rate first increased and then decreased, with an average annual growth rate of 4.79%. The range of carbon emissions (104 t) increased from [13.4, 425.4] in 1995 to [103.2, 1051.4] in 2019. The range of carbon emissions (t/10 000 yuan) decreased from [4.2, 9.7] in 2005 to that of 2019 [1.6, 5.0]. (2) Regional carbon emissions have always shown a spatial distribution pattern of high in the east and low in the west, high in the middle and low in the north and south. High-carbon counties and districts are mainly concentrated in Lanzhou and Xining and the surrounding densely populated and economically developed areas. The spatial difference is shrinking, and the spatial positive autocorrelation is gradually expanding. The local autocorrelation is relatively stable, dominated by high-high and low-low aggregation. The high-high aggregation is mainly concentrated in the main urban area of Lanzhou, and the low-low aggregation is distributed in Huangnan prefecture and Hainan prefecture. (3) The spatial differentiation of carbon emissions is affected by a variety of factors, and the influence of economic development level on the spatial differentiation of carbon emissions is always the strongest. The interaction between GDP and energy intensity, the enterprises number, industrial structure and urbanization level is the main driving force for the sustained growth of carbon emissions. |
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