云南大学 建筑与规划学院,云南,昆明,650091
纸质出版:2023
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李志英, 朱晓珊, 杨丽, 等. 云南省土地利用碳排放时空演变特征及影响因素[J]. 水土保持通报, 2023,43(5):297-303.
Li Zhiying, Zhu Xiaoshan, Yang Li, et al. Spatial-temporal Evolution Characteristics and Influencing Factors of Carbon Emissions in Yunnan Province Based on Land Use Changes[J]. Bulletin of Soiland Water Conservation, 2023, 43(5): 297-303.
李志英, 朱晓珊, 杨丽, 等. 云南省土地利用碳排放时空演变特征及影响因素[J]. 水土保持通报, 2023,43(5):297-303. DOI: 10.13961/j.cnki.stbctb.2023.05.035.
Li Zhiying, Zhu Xiaoshan, Yang Li, et al. Spatial-temporal Evolution Characteristics and Influencing Factors of Carbon Emissions in Yunnan Province Based on Land Use Changes[J]. Bulletin of Soiland Water Conservation, 2023, 43(5): 297-303. DOI: 10.13961/j.cnki.stbctb.2023.05.035.
[目的] 研究云南省土地利用碳排放的时空变化规律及其影响因素,为云南省优化土地利用结构,实现低碳发展目标提供理论依据。[方法] 基于云南省2005,2010,2015,2020年4期土地利用和化石能源消费数据,对全省碳排放效应进行测算,运用空间可视化和空间自相关研究云南省2005—2020年的碳排放时空变化规律和空间集聚特征,利用地理探测器对其影响因素进行分析。[结果] ①2005—2020年云南省建设用地增幅最大,动态变化度达7.90%。②区域净碳排放快速增加,年增长6.5%;碳排放空间特征为“中间高、四周低”;碳足迹在研究期内增长明显,碳生态承载力较为稳定,导致碳生态赤字日益升高。③人口规模、经济水平、产业结构、土地利用等促进了云南省各地州市碳排放的增加。[结论] 应保护或合理增加云南省林地等碳汇地类的面积并加强其动态监测;控制建设用地面积和能源消费总量;探索碳补偿机制并发挥碳汇地区的辐射效应。
[Objective] The temporal and spatial variation of carbon emissions due to land use changes and the factors influencing carbon emissions in Yunnan Province were analyzed in order to provide a theoretical basis for optimizing land use structure and achieving the low-carbon development goal in Yunnan Province. [Methods] Carbon emissions for Yunnan Province were calculated based on land use and fossil energy consumption data in Yunnan Province in 2005
2010
2015
and 2020. Spatial visualization and spatial autocorrelation were used to study the temporal and spatial variation and spatial agglomeration characteristics of carbon emissions from 2005 to 2020. The influencing factors were analyzed by geographical detectors. [Results] ① From 2005 to 2020
the area of construction land in Yunnan Province increased the most
with a dynamic change of 7.90%. ② Regional net carbon emissions increased rapidly
with an annual increase of 6.5%. The spatial pattern of carbon emissions was characterized as “higher in the central region and lower in the surrounding area”. The carbon footprint increased significantly during the study period
and the carbon ecological carrying capacity was relatively stable
resulting in an increasing carbon ecological deficit. ③ Population size
economic level
industrial structure
land use
etc. promoted the increase in carbon emissions for cities and counties in Yunnan Province. [Conclusion] In Yunnan Province
measures should be taken in the future to protect or reasonably increase the area of carbon sinks (such as forest land) and to strengthen dynamic monitoring
control the area of construction land and total energy consumption
explore the carbon compensation mechanism
and employ the radiation effect of carbon sink areas.
王少剑,高爽,黄永源,等.基于超效率SBM模型的中国城市碳排放绩效时空演变格局及预测[J].地理学报,2020,75(6):1316-1330.
Watson R T. Land Use, Land-use Change, and Forestry: A Special Report of the IPCC [M]. Cambridge: Cambridge University Press, 2000.
吕江.应对气候变化与生物多样性保护的国际规则协同: 演进、挑战与中国选择[J].北京理工大学学报(社会科学版),2022,24(2):50-60.
付琳,周泽宇,杨秀.适应气候变化政策机制的国际经验与启示[J].气候变化研究进展,2020,16(5):641-651.
葛全胜,戴君虎,何凡能,等.过去300年中国土地利用、土地覆被变化与碳循环研究[J].中国科学(D辑: 地球科学),2008,38(2):197-210.
赖力.中国土地利用的碳排放效应研究[D].江苏南京:南京大学,2010.
Su Meirong, Pauleit S, Yin Xuemei, et al. Greenhouse gas emission accounting for EU member states from 1991 to 2012[J]. Applied Energy, 2016,184:759-768.
Sohl T L, Sleeter B M, Zhu Zhiliang, et al. A land-use and land-cover modeling strategy to support a national assessment of carbon stocks and fluxes [J]. Applied Geography, 2012,34:111-124.
张秀梅,李升峰,黄贤金,等.江苏省1996年至2007年碳排放效应及时空格局分析[J].资源科学,2010,32(4):768-775.
严志翰,任丽燕,刘永强,等.浙江省碳排放时空格局及影响因素研究[J].长江流域资源与环境,2017,26(9):1427-1435.
魏燕茹,陈松林.福建省土地利用碳排放空间关联性与碳平衡分区[J].生态学报,2021,41(14):5814-5824.
舒心,夏楚瑜,李艳,等.长三角城市群碳排放与城市用地增长及形态的关系[J].生态学报,2018,38(17):6302-6313.
夏四友,杨宇.基于主体功能区的京津冀城市群碳收支时空分异与碳补偿分区[J].地理学报,2022,77(3):679-696.
Marchi M, Jørgensen S E, Pulselli F M, et al. Modelling the carbon cycle of Siena Province (Tuscany, Central Italy) [J]. Ecological Modelling, 2012,225:40-60.
Liu Jinxun, Vogelmann J E, Zhu Zhiliang, et al. Estimating California ecosystem carbon change using process model and land cover disturbance data: 1951—2000[J]. Ecological Modelling, 2011,222(14):2333-2341.
蓝家程,傅瓦利,袁波,等.重庆市不同土地利用碳排放及碳足迹分析[J].水土保持学报,2012,26(1):146-150.
Jo H K. Impacts of urban greenspace on offsetting carbon emissions for Middle Korea [J]. Journal of Environmental Management, 2002,64(2):115-126.
易丹,欧名豪,郭杰,等.土地利用碳排放及低碳优化研究进展与趋势展望[J].资源科学,2022,44(8):1545-1559.
李经路,曾天.基于Kaya方法的云南碳排放因素分析[J].科技管理研究,2016,36(19):260-266.
李经路,李晓玲.云南碳排放的变动趋势与影响因素研究[J].环境与可持续发展,2015,40(5):172-176.
方精云,郭兆迪,朴世龙,等.1981—2000年中国陆地植被碳汇的估算[J].中国科学(D辑: 地球科学),2007,37(6):804-812.
肖红艳,袁兴中,李波,等.土地利用变化碳排放效应研究: 以重庆市为例[J].重庆师范大学学报(自然科学版),2012,29(1):38-42.
李彦旻,沈育生,王世航.基于土地利用变化的安徽省陆地碳排放时空特征及效应[J].水土保持学报,2022,36(1):182-188.
彭文甫,周介铭,徐新良,等.基于土地利用变化的四川省碳排放与碳足迹效应及时空格局[J].生态学报,2016,36(22):7244-7259.
刘畅,祁毅,姚红,等.新时代背景下生态承载力研究要义与优化对策探讨[J].生态经济,2020,36(6):173-180.
许锋.基于Moran指数和谱图论的空间自相关测度方法优化[J].城市发展研究,2021,28(12):92-101.
王劲峰,徐成东.地理探测器: 原理与展望[J].地理学报,2017,72(1):116-134.
吾买尔艾力·艾买提卡力,阿巴拜克热·艾买提卡力,范昕,等.2000—2018年环鄱阳湖生态城市群碳排放时空分异规律及影响因素分析[J].生态经济,2021,37(6):51-57.
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