Research on the Spatio-Temporal Distribution and Influencing Factors of Carbon Emissions in China's Power Industry
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摘要: 2020年,中国提出了碳达峰、碳中和的目标,电力行业作为重点行业,其碳排放量是能否实现碳达峰、碳中和的重要因素。为分析中国电力行业碳排放特点及影响因素,利用传统统计方法对碳排放的时空计量数据进行分析,并利用灰色关联度分析法对电力行业不同能源的碳排放计量数据进行分析。从时间数据来看,我国电力行业碳排放量逐年上升,增长率持续波动;从空间数据来看,环渤海地区的电力行业碳排放量始终处于较高水平,内蒙古、新疆的电力行业碳排放量水平呈现上升趋势,江浙沪地区的电力行业碳排放量水平呈现下降趋势;从灰色关联度分析结果来看,原煤、其他石油产品、焦炉煤气、其他天然气的碳排放量对于电力行业的碳排放量影响比较大。Abstract: In 2020, China proposed the goal of peaking carbon emissions and achieving carbon neutrality. As a key industry, the power industry's carbon emission is an important factor in achieving carbon peaking and carbon neutrality. To analyze the characteristics and influencing factors of carbon emissions in China's power industry, traditional statistical methods are used to analyze the spatio-temporal data of carbon emissions, and the grey correlation analysis method is used to analyze the carbon emissions of different energy sources in the power industry. From the aspect of time data, the carbon emissions of China's power industry have been increasing year by year, and the growth rate continues to fluctuate; From the aspect of spatial data, the carbon emissions of the power industry in the Bohai Rim region have always been at a high level. The carbon emissions of the power industry in Inner Mongolia and Xinjiang are showing an upward trend, while the carbon emissions of the power industry in the Jiangsu-Zhejiang-Shanghai district are showing a downward trend; From the results of grey correlation analysis, it can be seen that the carbon emissions of raw coal, other petroleum products, coke oven gas, and other natural gas have a significant impact on the carbon emissions of the power industry.
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Key words:
- metrology /
- carbon emission /
- spatio-temporal data /
- grey correlation analysis /
- influencing factor
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表 1 灰色关联度分析结果
Table 1. Results of grey relation analysis
能源类型 原煤 洗精煤 其他洗煤 煤砖 焦炭 焦炉煤气 其他天然气 其他焦化产品 原油 关联度 0.968 0.540 0.820 0.749 0.675 0.858 0.832 0.664 0.483 能源类型 汽油 煤油 柴油 燃油 液化石油气 精炼天然气 其他石油产品 天然气 关联度 0.570 0.590 0.554 0.485 0.617 0.759 0.859 0.799 -
[1] 李延峰. 电力传输网络及其碳排放效应分析与协同优化模型研究[D]. 北京: 华北电力大学(北京), 2022. [2] 生态环境部. 关于做好2021、2022年度全国碳排放权交易配额分配相关工作的通知 [EB/OL]. (2023-03-15) [2023-11-20]. https://www.mee.gov.cn/xxgk2018/xxgk/xxgk03/202303/t20230315_1019707.html. [3] Liu W, Zuo B, Qu C, et al. A reasonable distribution of natural landscape: Utilizing green space and water bodies to reduce residential building carbon emissions[J]. Energy and Buildings, 2022, 267: 112150. doi: 10.1016/j.enbuild.2022.112150 [4] 蒋忠, 张亮, 王海峰, 等. 企业核算碳排放量不确定度评估[J]. 计量学报, 2022, 43(3): 420-426. doi: 10.3969/j.issn.1000-1158.2022.03.19 [5] 王靖添, 马晓明. 中国交通运输碳排放影响因素研究——基于双层次计量模型分析[J]. 北京大学学报 (自然科学版), 2021, 57(6): 1133-1142. [6] Xiang X, Ma X, Ma Z, et al. Python-LMDI: A tool for index decomposition analysis of building carbon emissions[J]. Buildings, 2022, 12(1): 83. doi: 10.3390/buildings12010083 [7] Zhang W, Li G, Guo F. Does carbon emissions trading promote green technology innovation in China?[J]. Applied Energy, 2022, 315: 119012. doi: 10.1016/j.apenergy.2022.119012 [8] Li R, Li L, Wang Q. The impact of energy efficiency on carbon emissions: evidence from the transportation sector in Chinese 30 provinces[J]. Sustainable Cities and Society, 2022, 82: 103880. doi: 10.1016/j.scs.2022.103880 [9] Bruckner B, Hubacek K, Shan Y, et al. Impacts of poverty alleviation on national and global carbon emissions[J]. Nature Sustainability, 2022, 5(4): 311-320. doi: 10.1038/s41893-021-00842-z [10] 杨红雄, 杨光. 基于现代化的中国省级碳排放时空演变及影响因素研究[J]. 气候变化研究进展, 2023, 19(4): 457-471. [11] 王瑛, 何艳芬. 中国省域二氧化碳排放的时空格局及影响因素[J]. 世界地理研究, 2020, 29(3): 512-522. doi: 10.3969/j.issn.1004-9479.2020.03.2018507 [12] 余碧莹, 赵光普, 安润颖, 等. 碳中和目标下中国碳排放路径研究[J]. 北京理工大学学报(社会科学版), 2021, 23(2): 17-24. [13] Zhou C, Lin X, Wang R, et al. Real-time carbon emissions monitoring of high-energy-consumption enterprises in Guangxi based on electricity big data[J]. Energies, 2023, 16(13): 5124. doi: 10.3390/en16135124 [14] 王婧婷, 王宇扬, 周明, 等. 考虑绿电交易的用户间接碳排放核算方法[J/OL]. 电网技术. https://doi.org/10.13335/j.1000-3673.pst.2023.0964. [15] 王春妍, 卢达, 李贺龙, 等. 电力碳排放计量网络溯源方法及计量分析[J/OL]. 电网技术. https://doi.org/10.13335/j.1000-3673.pst.2023.1572. [16] 李姚旺, 张宁, 杜尔顺, 等. 基于碳排放流的电力系统低碳需求响应机制研究及效益分析[J]. 中国电机工程学报, 2022, 42(8): 2830-2842. [17] Cai L, Duan J, Lu X, et al. Pathways for electric power industry to achieve carbon emissions peak and carbon neutrality based on LEAP model: A case study of state-owned power generation enterprise in China[J]. Computers & Industrial Engineering, 2022, 170: 108334. [18] Guan Y, Shan Y, Huang Q, et al. Assessment to China's recent emission pattern shifts[J]. Earth's Future, 2021, 9(11): e2021EF002241. doi: 10.1029/2021EF002241 [19] Shan Y, Huang Q, Guan D, et al. China CO2 emission accounts 2016–2017[J]. Scientific data, 2020, 7(1): 54. doi: 10.1038/s41597-020-0393-y [20] Shan Y, Guan D, Zheng H, et al. China CO2 emission accounts 1997–2015[J]. Scientific data, 2018, 5(1): 1-14. doi: 10.1038/s41597-018-0002-5 [21] Shan Y, Liu J, Liu Z, et al. New provincial CO2 emission inventories in China based on apparent energy consumption data and updated emission factors[J]. Applied Energy, 2016, 184: 742-750. doi: 10.1016/j.apenergy.2016.03.073 [22] 环境保护部. 中华人民共和国环境保护法 [EB/OL]. (2014-04-25) [2023-11-28]. http://www.npc.gov.cn/npc/c1773/c2518/c27694/c27698/201905/t20190521_208293.html. [23] 国家能源局. 三部委联合发布《能源行业加强大气污染防治工作方案》 [EB/OL]. (2014-05-16) [2023-11-28]. https://www.nea.gov.cn/2014-05/16/c_133339262.htm. [24] 国家发展和改革委员会. 中华人民共和国国家发展和改革委员会令 [EB/OL]. (2012-10-14) [2023-11-28]. https://www.ndrc.gov.cn/xxgk/zcfb/fzggwl/201210/t20121031_960743.html.