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基于江西省县域碳核算的碳排放时空变化及特征要素分析

符家齐 甘然屹 潘飞 许琳燕 尧骞慧 钱挺 刘雪梅

符家齐, 甘然屹, 潘飞, 许琳燕, 尧骞慧, 钱挺, 刘雪梅. 基于江西省县域碳核算的碳排放时空变化及特征要素分析[J]. 环境工程, 2026, 44(1): 226-237. doi: 10.13205/j.hjgc.202601024
引用本文: 符家齐, 甘然屹, 潘飞, 许琳燕, 尧骞慧, 钱挺, 刘雪梅. 基于江西省县域碳核算的碳排放时空变化及特征要素分析[J]. 环境工程, 2026, 44(1): 226-237. doi: 10.13205/j.hjgc.202601024
FU Jiaqi, GAN Ranyi, PAN Fei, XU Linyan, YAO Qianhui, QIAN Ting, LIU Xuemei. Analysis on carbon emissions spatiotemporal variations and characteristic factors: a county-level carbon accounting case in Jiangxi Province[J]. ENVIRONMENTAL ENGINEERING , 2026, 44(1): 226-237. doi: 10.13205/j.hjgc.202601024
Citation: FU Jiaqi, GAN Ranyi, PAN Fei, XU Linyan, YAO Qianhui, QIAN Ting, LIU Xuemei. Analysis on carbon emissions spatiotemporal variations and characteristic factors: a county-level carbon accounting case in Jiangxi Province[J]. ENVIRONMENTAL ENGINEERING , 2026, 44(1): 226-237. doi: 10.13205/j.hjgc.202601024

基于江西省县域碳核算的碳排放时空变化及特征要素分析

doi: 10.13205/j.hjgc.202601024
基金项目: 

国家自然科学基金重点项目“农业汇水区反硝化脱氮速率对周丛生物的响应机制与高光谱识别”(41867020);江西省研究生创新专项资金项目“电场作用下柠檬酸对城市固体废物焚烧飞灰中重金属和氯的去除特征研究”(YC2023-S487)

详细信息
    作者简介:

    符家齐(1997—),男,硕士,主要研究方向为环境规划与应用。185723603@qq.com

    通讯作者:

    刘雪梅(1978—),女,教授,主要研究方向为农业废弃物资源化利用。475483235@qq.com

Analysis on carbon emissions spatiotemporal variations and characteristic factors: a county-level carbon accounting case in Jiangxi Province

  • 摘要: 基于类NPP/VIIRS并结合MOD17A3HGF v061 NPP植被净初级生产力数据,计算2007—2022年江西省县域碳排放量及碳汇量,研究其碳排放时空变化情况及碳排放特征要素,并分析地理区位、经济状况、产业结构等对区(县、市)的碳排放时空及特征要素的影响。研究结果显示:1)通过拟合夜间灯光数据与碳排量得到R2=0.8477,并以此为基础进行县域反演,同时碳排放统计值与模拟值进行拟合,得到R2=0.9423,其碳汇量对比中国碳核算数据库CEADs的变化率在±0.5%以内,均符合精度要求。2)江西省县域碳排整体呈现逐年递增的态势,以地级市为中心向周围区域辐射递减,而碳汇量呈现波动增长的态势,碳排放量年均增长率为5.05%,碳汇量年均增长率则只有0.06%,但整体仍呈现明显的净碳汇区。净碳排量与碳汇量的变化趋势一致且平稳。3)碳排放强度、碳补偿率、经济贡献系数、生态承载系数整体均呈现下降趋势。研究结果表面,合理规划江西省整体发展及产业布局,促进产业结构的升级及低碳转型,对江西省加快实现“双碳”目标具有重要意义。
  • [1] BENJAMIN G,DIMITRIOS G,P N J. The carbon footprint of household energy use in the United States[J]. Proceedings of the National Academy of Sciences of the United States of America,2020,117(32):19122-19130.
    [2] CHEN J,GAO M,CHENG S,et al. County-level CO2 emissions and sequestration in China during 1997—2017[J]. Scientific Data,2020,7(1):1-12.
    [3] YE X C,MENG Y K,XU L G,et al. Net primary productivity dynamics and associated hydrological driving factors in the floodplain wetland of China's largest freshwater lake[J]. Science of the Total Environment,2019,659:302-313.
    [4] FRIENDLINGSTEIN P,O'SULLIVAN M,JONES M W,et al. Global carbon budget 2020[J]. Earth System Science Data,2020,12(4):3269-3340.
    [5] HARRIS N L,GIBBS D A,BACCINI A,et al. Global maps of twenty-first century forest carbon fluxes[J]. Nature Climate Change,2021,11(3):234-241.
    [6] HUANG X J,ZHANG X Y,LU X H,et al. Land development and utilization for carbon neutralization[J]. Journal of Natural Resources,2021,36(12):2995-3006. 黄贤金,张秀英,卢学鹤,等. 面向碳中和的中国低碳国土开发利用[J]. 自然资源学报,2021,36(12):2995-3006.
    [7] FU C,YU G R,FANG H J,et al. Effects of land use and cover change on terrestrial carbon balance of China[J]. Progress in Geography,2012,31(1):88-96. 付超,于贵瑞,方华军,等. 中国区域土地利用/覆被变化对陆地碳收支的影响[J]. 地理科学进展,2012,31(1):88-96.
    [8] ZHANG H L. Temporal and spatial variation characteristics and driving forces of carbon emission from energy consumption in China using DMSP/OLS nighttime light[D]. Lanzhou:Lanzhou University,2019. 张慧琳. 基于DMSP/OLS夜间灯光数据的中国能源消费碳排放时空变化特征及驱动力研究[D]. 兰州:兰州大学,2019.
    [9] SU Y X. Study on the carbon emissions from energy consumption in China using DMSP/OLS night light imageries[D]. Guangzhou:University of Chinese Academy of Sciences(Guangzhou Institute of Geochemistry),2015. 苏泳娴. 基于DMSP/OLS夜间灯光数据的中国能源消费碳排放研究[D]. 广州:中国科学院大学(广州地球化学研究所),2015.
    [10] WANG L W,FENG C C. Spatial expansion pattern and dynamic mechanism of Beijing-Tianjin-Hebei urban agglomeration during the transition period:based on nighttime light data method[J]. Acta Geographica Sinica,2016,71(12):2155-2169. 王利伟,冯长春. 转型期京津冀城市群空间扩展格局及其动力机制:基于夜间灯光数据方法[J]. 地理学报,2016,71(12):2155-2169.
    [11] SI W T,ZHANG N H,YE H P,et al. Urbanization process of the Beijing-Tianjin-Hebei urban agglomeration based on long-term nighttime light data[J]. Resources Science,2022,44(10):2114-2124. 司文涛,张宁慧,叶海鹏,等. 基于长时间序列夜间灯光数据的京津冀城市群城市化过程[J]. 资源科学,2022,44(10):2114-2124.
    [12] WANG Y K,ZHAO M X,RONG L H. Spatial expansion characteristics and driving forces of Hohhot-Baotou-Ordos urban agglomeration based on night light data[J]. Areal Research and Development,2021,40(3):43-49. 王彦开,赵渺希,荣丽华. 基于夜间灯光数据的呼包鄂城市群空间扩张特征及驱动力研究[J]. 地域研究与开发,2021,40(3):43-49.
    [13] WU L,ZHOU T G,WEN L,et al. Study on spatio-temporal relationship between PM2.5 and urbanization based on remote sensing data:a case study of Chengdu-Chongqing urban agglomeration[J]. Resources and Environment in the Yangtze Basin,2018,27(9):2142-2152. 吴浪,周廷刚,温莉,等. 基于遥感数据的PM2.₅与城市化的时空关系研究:以成渝城市群为例[J]. 长江流域资源与环境,2018,27(9):2142-2152.
    [14] YANG N,WU L L,DENG S L,et al. Spatialization method of provincial statistical GDP data based on DMSP/OLS night lighting data:a case study of Guangxi Zhuang Autonomous Region[J]. Geography and Geo-Information Science,2014,30(4):108-111. 杨妮,吴良林,邓树林,等. 基于DMSP/OLS夜间灯光数据的省域GDP统计数据空间化方法:以广西壮族自治区为例[J]. 地理与地理信息科学,2014,30(4):108-111.
    [15] HU L L,LIU H. Evaluation of quality of China's provincial GDP data based on night light data[J]. Statistics& Decision,2012,38(7):5-9. 胡兰丽,刘洪. 基于夜间灯光数据的中国省域GDP数据质量评估[J]. 统计与决策,2012,38(7):5-9.
    [16] ZHOU X Y. Estimation of carbon emissions from energy consumption and analysis of spatio-temporal distribution in Guangdong Province based on nighttime light data[D]. Chengdu:Chengdu University of Technology,2018. 周星勇. 基于夜间灯光数据的广东省能源消耗碳排放估计与时空分布分析[D]. 成都:成都理工大学,2018.
    [17] LIU X Z,YANG X. The accuracy of nighttime light data to estimate China's provincial carbon emissions:a comparison with carbon emissions allocated by international carbon database[J]. Remote Sensing Technology and Application,2022,37(2):319-332. 刘贤赵,杨旭. 夜间灯光数据估算中国省域碳排放与国际碳数据库分配的碳排放比较[J]. 遥感技术与应用,2022,37(2):319-332.
    [18] WU Y,LI H B. Spatial change and correlations of desakota regions in a metropolitan area using NPP/VIIRS nighttime light data:a case study of Wuhan City[J]. Progress in Geography,2020,39(1):13-23. 吴燕,李红波. 大都市城乡融合区空间演进及内在关联性测度:基于武汉市夜间灯光数据[J]. 地理科学进展,2020,39(1):13-23.
    [19] BAI H T,MA M G,YAN R,et al. Evaluation of urban expansion in Chongqing City based on nighttime light data[J]. Remote Sensing Technology and Application,2019,34(1):216-224. 白贺庭,马明国,阎然,等. 基于夜间灯光数据的重庆市城市扩张研究[J]. 遥感技术与应用,2019,34(1):216-224.
    [20] QIN Y,LIU K M. Authenticity of China's prefecture-level city GDP data:test based on NPP-VIIRS night light data[J]. Statistics& Decision,2019,35(13):19-23. 秦永,刘凯敏. 中国地级市GDP数据的真实性:基于NPP-VIIRS夜间灯光数据的检验[J]. 统计与决策,2019,35(13):19-23.
    [21] ZHANG Y,ZHANG Y X,ZHANG Y J,et al. Study on the spatialization of carbon emissions in Xi'an based on Luojia-01 nighttime light data[J]. Remote Sensing Technology and Application,2023,38(4):869-879. 张瑶,张宇鑫,张勇建,等. 基于珞珈一号夜间灯光数据西安市碳排放空间化研究[J]. 遥感技术与应用,2023,38(4):869-879.
    [22] CHEN Q,HOU X Y,WU L. Comparison of population spatialization models based on land use data and DMSP/OLS data respectively:a case study in the efficient ecological economic zone of the Yellow River Delta[J]. Human Geography,2014,29(5):94-100. 陈晴,侯西勇,吴莉. 基于土地利用数据和夜间灯光数据的人口空间化模型对比分析:以黄河三角洲高效生态经济区为例[J]. 人文地理,2014,29(5):94-100.
    [23] SUN G Y,WANG S,XIAO L. Research on carbon emission from energy consumption and influencing factors in the upper reaches of the Yangtze River based on nightlight data[J]. Areal Research and Development,2020,39(4):159-162,174. 孙贵艳,王胜,肖磊. 基于夜间灯光数据的长江上游地区能源消费碳排放及影响因素研究[J]. 地域研究与开发,2020,39(4):159-162,174.
    [24] CHEN Z J,LIU Y M,LIU X,et al. Research on carbon emission peak in Yangtze River economic zone with steady economic growth:based on data of global night-time light[J]. Journal of Natural Resources,2018,33(12):2213-2222. 陈志建,刘月梅,刘晓,等. 经济平稳增长下长江经济带碳排放峰值研究:基于全球夜间灯光数据的视角[J]. 自然资源学报,2018,33(12):2213-2222.
    [25] LÜ Q,LIU H B. Multiscale spatio-temporal characteristics of carbon emission of energy consumption in Yellow River Basin based on the nighttime light datasets[J]. Economic Geography,2020,40(12):12-21. 吕倩,刘海滨. 基于夜间灯光数据的黄河流域能源消费碳排放时空演变多尺度分析[J]. 经济地理,2020,40(12):12-21.
    [26] NIU Y W,ZHAO X C,HU Y J. Spatial variation of carbon emissions from county land use in Chang-Zhu-Tan area based on NPP-VIIRS night light[J]. Acta Scientiae Circumstantiae,2021,41(9):3847-3856. 牛亚文,赵先超,胡艺觉. 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[J]. 环境科学学报,2021,41(9):3847-3856.
    [27] ZHANG B,WEI D Q,DING Y,et al. Research on cities' carbon emissions and their spatiotemporal evolution coupled with nighttime light image and land use data in the Pearl River Basin[J]. Advances in Earth Science,2024,39(3):317-328. 张斌,卫丹琪,丁乙,等. 基于夜间灯光和土地利用的珠江流域城市碳排放估算及其时空动态特征研究[J]. 地球科学进展,2024,39(3):317-328.
    [28] WU N,SHEN L,ZHONG S. Spatio-temporal pattern of carbon emissions based on nightlight data of the Shanxi-Shaanxi-Inner Mongolia region of China[J]. Journal of Geo-information Science,2019,21(7):1040-1050. 武娜,沈镭,钟帅. 基于夜间灯光数据的晋陕蒙能源消费碳排放时空格局[J]. 地球信息科学学报,2019,21(7):1040-1050.
    [29] ZHU N,ZHANG Y F,WEI H J. Spatial differences of carbon emissions intensity on the county level:a case study of Yulin city,Shaanxi Province[J]. Areal Research and Development,2014,33(6):164-169. 朱妮,张艳芳,位贺杰. 县域尺度下能源产区能源消费碳排放强度空间分异:以陕西省榆林市为例[J]. 地域研究与开发,2014,33(6):164-169.
    [30] DU H B,WEI W,ZHANG X Y,et al. Spatio-temporal evolution and influencing factors of energy-related carbon emissions in the Yellow River Basin:based on the DMSP/OLS and NPP/VIIRS nighttime light data[J]. Geographical Research,2021,40(7):2051-2065. 杜海波,魏伟,张学渊,等. 黄河流域能源消费碳排放时空格局演变及影响因素:基于DMSP/OLS与NPP/VIIRS夜间灯光数据[J]. 地理研究,2021,40(7):2051-2065.
    [31] ZHU E Y. Research on the dynamic spatial-temporal pattern of carbon emissions in Zhejiang Province and its response to urbanization[D]. Hangzhou:Zhejiang University,2020. 朱恩燕. 浙江省碳排放时空格局动态及其对城镇化的响应研究[D]. 杭州:浙江大学,2020.
    [32] LONG Z. Spatial-temporal pattern of carbon emissions at the county level and optimization of carbon balance zoning[D]. Lanzhou:Lanzhou University,2022. 龙志. 县域尺度碳排放时空格局与碳平衡分区优化[D]. 兰州:兰州大学,2022.
    [33] CHEN G G. Research on the spatial-temporal distribution characteristics and influencing factors of carbon emissions from energy consumption at the county level in the Lanzhou-Xining Urban Agglomeration[D]. Lanzhou:Lanzhou University of Finance and Economics,2022. 陈刚刚. 兰西城市群县域能源消费碳排放时空分布特征及影响因素研究[D]. 兰州:兰州财经大学,2022.
    [34] GU Y Y,QIAO X N,FAN L X,et al. Spatial analysis of carbon emissions from regional energy consumption based on night light data[J]. Science of Surveying and Mapping,2017,42(2):140-146. 顾羊羊,乔旭宁,樊良新,等. 夜间灯光数据的区域能源消费碳排放空间化[J]. 测绘科学,2017,42(2):140-146.
    [35] MAO X G,CHEN W Q,HU S,et al. Simulation and analysis of net primary productivity in Harbin with ecological process model[J]. Journal of Northeast Forestry University,2017,45(7):55-60. 毛学刚,陈文曲,胡屾,等. 基于生态过程模型的哈尔滨市净初级生产力模拟和分析[J]. 东北林业大学学报,2017,45(7):55-60.
    [36] CHEN J,FAN W,LI D,et al. Driving factors of global carbon footprint pressure:based on vegetation carbon sequestration[J]. Applied Energy,2020,267:114897.
    [37] LI J B,CHEN H M,ZHANG C L,et al. Spatiotemporal evolution characteristics of carbon sources and carbon sinks and carbon balance zoning in the Yangtze River Delta Region[J]. Environmental Science,2024,45(7):4090-4100. 李建豹,陈红梅,张彩莉,等. 长三角地区碳源碳汇时空演化特征及碳平衡分区[J]. 环境科学,2024,45(7):4090-4100.
    [38] LI Y Y,WEI W,ZHOU J J,et al. Changes in land use carbon emissions and coordinated zoning in China[J]. Environmental Science,2023,44(3):1267-1276. 李缘缘,魏伟,周俊菊,等. 中国土地利用碳排放变化及协调分区[J]. 环境科学,2023,44(3):1267-1276.
    [39] RONG T,ZHANG P,JING W,et al. Carbon dioxide emissions and their driving forces of land use change based on economic contributive coefficient(ECC)and ecological support coefficient(ESC)in the Lower Yellow River Region(1995-2018)[J]. Energies,2020,13(10):2568-2585.
    [40] ZHANG Y,LIN K L,JI X Z,et al. Construction of carbon compensation mechanism in urban area from the perspective of carbon neutrality:taking Shaanxi Province as an example[J]. Journal of Anhui Agricultural Sciences,2023,51(9):58-64. 张宇,蔺康莉,纪欣卓,等. 碳中和视角下市域碳补偿机制构建:以陕西省为例[J]. 安徽农业科学,2023,51(9):58-64.
    [41] HAO R J,WEI W,LIU C F,et al. Spatialization and spatio-temporal dynamics of energy consumption carbon emissions in China[J]. Environmental Science,2022,43(11):5305-5314. 郝瑞军,魏伟,刘春芳,等. 中国能源消费碳排放的空间化与时空动态[J]. 环境科学,2022,43(11):5305-5314.
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  • 收稿日期:  2025-01-02
  • 网络出版日期:  2026-02-26
  • 刊出日期:  2026-01-22

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