气象和人类活动对长江经济带植被固碳的影响

沈园, 康平, 刘琰琰, 冯浩鹏, 李路丹, 秦文霜, 修蒙

中国环境科学 ›› 2025, Vol. 45 ›› Issue (12) : 6943-6953.

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中国环境科学 ›› 2025, Vol. 45 ›› Issue (12) : 6943-6953.
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气象和人类活动对长江经济带植被固碳的影响

  • 沈园1, 康平1,2, 刘琰琰1,2, 冯浩鹏1,3,4, 李路丹1, 秦文霜1, 修蒙1,2
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Impacts of meteorological factors and human activities on vegetation carbon sequestration in the Yangtze River Economic Belt

  • SHEN Yuan1, KANG Ping1,2, LIU Yan-yan1,2, FENG Hao-peng1,3,4, LI Lu-dan1, QIN Wen-shuang1, XIU Meng1,2
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摘要

基于2001~2020年的植被遥感数据和气象数据,利用趋势分析、变异系数、二阶偏相关等方法对长江经济带植被固碳能力(CSC)的时空变化特征及影响因子进行了分析,并结合偏导数方法定量评估具体气象因子(气温、降水、辐射)及人类活动对长江经济带不同区域CSC的贡献量.结果表明:①近20年来长江经济带多年平均CSC为2505.4gCO2/m2,CSC整体呈波动上升趋势,多年平均增长趋势为8.9gCO2/(m2·a),其中极显著增长区域(P<0.01)的面积占比25.6%.②长江经济带气温和降水均在16.0%以上的区域表现为显著增加趋势,与CSC的关系主要为正偏相关性;辐射则在6.3%的区域表现为显著减少趋势,且辐射与CSC的关系主要为负偏相关性.③气象因子和人类活动在整体上对CSC均表现为正贡献(使CSC增加),其中人类活动的贡献量最大,达到了+16.35gCO2/(m2·a) (86.7%);其次是降水的贡献(+1.28gCO2/(m2·a),6.77%),在上游省份均表现为正贡献,在中、下游省份则更多表现为负贡献;辐射与气温对于CSC的贡献均较少,辐射在贵州与上海两省表现为负贡献,气温则在所有省份均表现为正贡献.

Abstract

Based on vegetation remote sensing and meteorological data from 2001 to 2020, the spatiotemporal variations and influencing factors of vegetation carbon sequestration capacity(CSC) in the Yangtze River Economic Belt(YREB) were analyzed using trend analysis, coefficient of variation, and second-order partial correlation. Furthermore, the partial derivative method was applied to quantitatively assess the contributions of specific meteorological factors (temperature, precipitation, radiation) and human activities to CSC across different sub-regions of the YREB. The results showed that: (1) The multi-year average CSC in the YREB was 2505.4gCO2/m2, with an overall fluctuating upward trend and a multi-year average growth rate of 8.9gCO2/(m2·a). Areas with extremely significant increases (P<0.01) accounted for 25.6% of the region. (2) Significant increased trends in both temperature and precipitation were observed in more than 16.0% of the area, and they were mainly positively correlated with CSC. In contrast, a significant decreased trend in radiation was observed in 6.3% of the area, and it was mainly negatively correlated with CSC. (3) Both meteorological factors and human activities exerted overall positive contributions to CSC (leading to increases in CSC), among which the largest contribution was attributed to human activities (+16.35gCO2/(m2·a), 86.7%), followed by precipitation (+1.28gCO2/ (m2·a), 6.77%), which contributed positively in the upstream provinces but negatively in the middle and lower reaches. Smaller contributions were attributed to radiation and temperature; a negative contribution of radiation was observed in Guizhou and Shanghai, while positive contributions of temperature were found in all provinces.

关键词

植被固碳能力 / 长江经济带 / 定量贡献评估 / 偏导数方法

Key words

vegetation carbon sequestration capacity(CSC) / Yangtze River Economic Belt / quantitative contribution assessment / partial derivative method

引用本文

导出引用
沈园, 康平, 刘琰琰, 冯浩鹏, 李路丹, 秦文霜, 修蒙. 气象和人类活动对长江经济带植被固碳的影响[J]. 中国环境科学. 2025, 45(12): 6943-6953
SHEN Yuan, KANG Ping, LIU Yan-yan, FENG Hao-peng, LI Lu-dan, QIN Wen-shuang, XIU Meng. Impacts of meteorological factors and human activities on vegetation carbon sequestration in the Yangtze River Economic Belt[J]. China Environmental Science. 2025, 45(12): 6943-6953
中图分类号: X171    P966.1/.7   

参考文献

[1] 曹建飞,韩延玲,刘慧龙.数字经济发展如何影响城市碳排放?——来自中国283个地级市的经验数据 [J]. 生态经济, 2024,40(2): 39-51. Cao J F, Han Y T, Liu H L, et al. Can the development of the digital economy affect carbon emissions?: An empirical evidence from 288 prefecture-level cities in China [J]. Ecological Economy, 2024,40(2): 39-51.
[2] 宋 梅,王子莎,陈 晨,等.“双碳”目标下黄河流域资源型城市碳足迹时空特征及组态路径 [J]. 中国环境科学, 2025,45(8):4677-4687. Song M, Wang Z S, Chen C, et al. Spatiotemporal characteristics and configuration paths of carbon footprint in resource-based cities of the Yellow River Basin under the “dual-carbon” goal [J]. China Environmental Science, 2025,45(8):4677-4687.
[3] 胡鞍钢.中国实现2030年前碳达峰目标及主要途径 [J]. 北京工业大学学报(社会科学版), 2021,21(3):1-15. Hu A G. China’s achievement of carbon peak by 2030 and major pathways [J]. Journal of Beijing University of Technology (Social Sciences Edition), 2021,21(3):1-15.
[4] 张祥华.碳中和目标下森林碳汇供给的潜力与优化路径 [D]. 哈尔滨:东北林业大学, 2023. Zhang X H. Potential and optimization path of forest carbon sink supply under the carbon neutrality goal [D]. Harbin: Northeast Forestry University, 2023.
[5] Feng H P, Kang P, Deng Z C, et al. The impact of climate change and human activities to vegetation carbon sequestration variation in Sichuan and Chongqing [J]. Environmental Research, 2023,238: 117138.
[6] 李 媛.陆地植被净初级生产力估算及影响因素研究现状 [J]. 宁夏大学学报(自然科学版), 2018,39(4):362-366. Li Y. Estimation of terrestrial vegetation net primary productivity and research status of influencing factors [J]. Journal of Ningxia University (Natural Science Edition), 2018,39(4):362-366.
[7] Lieth H, Whittaker R H. Primary productivity of the biosphere [M]. New York: Springer-Verlag, 1975.
[8] 朱文泉,潘耀忠,张锦水.中国陆地植被净初级生产力遥感估算 [J]. 植物生态学报, 2007,31(3):413-424. Zhu W Q, Pan Y Z, Zhang J S. Remote sensing estimation of terrestrial vegetation net primary productivity in China [J]. Acta Phytoecologica Sinica, 2007,31(3):413-424.
[9] 唐 霞.长江经济带植被生态系统NEP指数时空特征及预测研究 [D]. 荆州:长江大学, 2023. Tang X. Spatio-temporal characteristics and prediction study of vegetation ecosystem NEP index in the Yangtze River Economic Belt [D]. Jinzhou: Yangtze University, 2023.
[10] 屈志光,杨伯元,胡李丹,等.长江经济带土地利用模拟与扩绿增长协同发展 [J]. 中国环境科学, 2025,45(1):465-476. Qu Z G, Yang B Y, Hu L D, et al. Simulation of land use and synergistic development between promoting green expansion and economic growth in the Yangtze River Economic Belt [J]. China Environmental Science, 2025,45(1):465-476.
[11] 林明裕,崔兴华.碳排放绩效的空间关联网络结构及其形成机制——以长江经济带为例 [J]. 中国环境科学, 2024,44(5):2867-2878. Lin M Y, Cui X H. The spatial correlation network structure and formation mechanism of carbon emission performance: A case study of the Yangtze River Economic Belt [J]. China Environmental Science, 2024,44(5):2867-2878.
[12] Chu X, Zhan J Y, Li Z H, et al. Assessment on forest carbon sequestration in the Three-North Shelterbelt Program region, China [J]. Journal of Cleaner Production, 2019,215:382-389.
[13] Dai E, Huang Y, Wu Z, et al. Analysis of spatio-temporal features of a carbon source/sink and its relationship to climatic factors in the Inner Mongolia grassland ecosystem [J]. Journal of Geographical Sciences, 2016,26(3):297-312.
[14] 王 君.2023年长江经济带区域拉动全国经济增长2.6个百分点 [N]. 中国信息报, 2024-02-20(01). Wang J. The Yangtze River Economic Belt Region drove national economic growth by 2.6percentage points in 2023 [N]. China Information News, 2024-02-20(01).
[15] 范育蕾,夏哲一,魏 雪,等.四个时期长江经济带湿地生态系统服务对景观格局响应研究 [J]. 湿地科学, 2024,22(1):139-147. Fan Y L, Xia Z Y, Wei X, et al. Response of wetland ecosystem services to landscape patterns in the Yangtze River Economic Belt across four periods [J]. Wetland Science, 2024,22(1):139-147.
[16] 周 泓,刘 洋,张雪瑶,等.生态优先推动长江经济带绿色发展——《长江经济带发展规划纲要》初步解读 [J]. 环境与可持续发展, 2016,41(6):191-192. Zhou H, Liu Y, Zhang X Y, et al. Ecological priority promotes green development in the Yangtze River Economic Belt: A preliminary interpretation of the “Yangtze River Economic Belt Development Plan Outline” [J]. Environment and Sustainable Development, 2016,41(6): 191-192.
[17] 邓玉娇,王捷纯,徐 杰,等.广东省植被固碳量时空变化及气象贡献率研究 [J]. 生态环境学报, 2022,31(1):1-8. Deng Y J, Wang J C, Xu J, et al. Spatio-temporal variation and meteorological contribution rate of vegetation carbon sequestration in Guangdong Province [J]. Journal of Ecology and Environment, 2022, 31(1):1-8.
[18] Piao S L, Fang J Y, Ciais P, et al. The carbon balance of terrestrial ecosystems in China[J]. Nature, 2009,458(7241):1009-1013.
[19] Gocic M, Trajkovic S. Analysis of changes in meteorological variables using Mann-Kendall and Sen’s slope estimator statistical tests in Serbia [J]. Global and Planetary Change, 2013,100:172-182.
[20] 王文森.变异系数——一个衡量离散程度简单而有用的统计指标 [J]. 中国统计, 2007,(6):41-42. Wang W S. Coefficient of variation: A simple and useful statistical measure of dispersion [J]. China Statistics, 2007,(6):41-42.
[21] Forkman J. Estimator and tests for common coefficients of variation in normal distributions [J]. Communications in Statistics -Theory and Methods, 2009,38(2):233-251.
[22] Liu Q, Fu Y H, Zeng Z Z, et al. Temperature, precipitation, and insolation effects on autumn vegetation phenology in temperate China [J]. Global Change Biology, 2015,22(2):644-655.
[23] Roderick M L, Rotstayn l D, Farquhar G D, et al. On the attribution of changing pan evaporation [J]. Geophysical Research Letters, 2007, 34(17):1-7.
[24] 易 浪,孙 颖,尹少华,等.2000~2019年长江流域植被覆盖时空演化及其驱动因素 [J]. 生态学报, 2023,43(2):798-811. Yi L, Sun Y, Yin S H, et al. Spatio-temporal evolution and driving factors of vegetation cover in the Yangtze River Basin from 2000 to 2019 [J]. Acta Ecologica Sinica, 2023,43(2):798-811.
[25] 赵乾佐,张 璇,费俊源,等.长江流域不同植被对2022年极端气象干旱事件的响应 [J]. 中国环境科学, 2024,44(9):5134-5144. Zhao Q Z, Zhang X, Fei J Y, et al. Vegetation responses to extreme meteorological drought event of 2022in the Yangtze River Basin [J]. China Environmental Science, 2024,44(9):5134-5144.
[26] 郭敏杰,张亭亭,张建军,等.1982~2006年黄土高原地区植被覆盖度对气候变化的响应 [J]. 水土保持研究, 2014,21(5):35-40,48. Guo M J, Zhang T T, Zhang J J, et al. Response of vegetation cover to climate change in the Loess Plateau Region from 1982 to 2006 [J]. Research of Soil and Water Conservation, 2014,21(5):35-40+48
[27] 鲁铁定,章 园,曾思婷,等.江西省NPP估算及其与气候因子的关联分析-基于改进CASA模型 [J]. 中国环境科学, 2025,45(1):369-378. Lu T D, Zhang Y, Zeng S T, et al. Estimation of net primary productivity and correlation study with climate parameters in Jiangxi Province using the enhanced CASA model [J]. China Environmental Science, 2025,45(1):369-378.
[28] 王志鹏,张宪洲,何永涛,等.2000~2015年青藏高原草地归一化植被指数对降水变化的响应 [J]. 应用生态学报, 2018,29(1):75-83. Wang Z P, Zhang X Z, He Y T, et al. Response of normalized difference vegetation index to precipitation change in alpine grasslands on the Qinghai-Tibet Plateau from 2000 to 2015 [J]. Acta Ecologica Sinica, 2018,29(1):75-83.
[29] 高 斌.长江上游极端降水非平稳拟合及致灾效应分析 [J]. 水利水电技术(中英文), 2025,56(2):1-14. Gao B. Nonstationary fitting and disaster-causing effect analysis of extreme precipitation in the upper Yangtze River [J]. Hydraulic and Hydro-power Technology (Chinese-English), 2025,56(2):1-14.
[30] 贯 君,张少鹏,任 月,等.中国农业净碳汇时空分异与影响因素演进分析 [J]. 中国环境科学, 2024,44(2):1158-1170. Guan J, Zhang S P, Ren Y, et al. Random forest model-assisted evaluation of spatiotemporal differentiation of China’s agricultural net carbon sink and evolution of influencing factors [J]. China Environmental Science, 2024,44(2):1158-1170.
[31] 秦梦祯.重庆耐阴园林树种的评价及植物配置设计研究 [D]. 重庆:重庆交通大学, 2023. Qin M Z. Evaluation of shade-tolerant garden tree species and planting design research in Chongqing [D]. Chongqing: Chongqing Jiaotong University, 2023.
[32] 郭少杰.中国陆地植被固碳量的时空演变与影响因素研究 [D]. 杨凌:西北农林科技大学, 2023. Guo S J. Spatio-temporal evolution and influencing factors of terrestrial vegetation carbon sequestration in China [D]. Yangling: Northwest A&F University, 2023.
[33] 杨 屹,苏 越.关中平原城市群碳收支时空分异特征及碳平衡驱动因素 [J]. 中国环境科学, 2025,45(7):4034-4051. Yang Y, Su Y. Spatial and temporal variability of carbon budget in Guanzhong Plain urban agglomeration and the driving factors of carbon balance [J]. China Environmental Science, 2025,45(7):4034-4051.
[34] 路 广.近代黄河三角洲净初级生产力时空变化分析 [D]. 济南:山东师范大学, 2018. Lu G. Spatio-temporal analysis of net primary productivity in the Modern Yellow River Delta [D]. Jinan: Shandong Normal University, 2018.
[35] 曹 云,张称意,孙应龙,等.2000~2020年华北地区植被固碳能力时空变化特征及其气象影响分析 [J]. 生态学报, 2023,43(9):3488-3499. Cao Y, Zhang C Y, Sun Y L, et al. Spatio-temporal variation characteristics and meteorological impact analysis of vegetation carbon sequestration capacity in North China from 2000 to 2020 [J]. Acta Ecologica Sinica, 2023,43(9):3488-3499.
[36] 张宏思,陈之光,张 翔,等.1977~2017年三江源区植被固碳量及影响因素评估 [J]. 南开大学学报(自然科学版), 2021,54(4):87-100, 107. Zhang H S, Chen Z G, Zhang X, et al. Assessment of vegetation carbon sequestration and its influencing factors in the Sanjiangyuan Region from 1977 to 2017 [J]. Journal of Nankai University (Natural Science Edition), 2021,54(4):87-100,107.
[37] 孔俊杰,刘海新,王 晓,等.气候变化和人类活动对太行山区植被NPP变化的影响 [J]. 山东林业科技, 2024,54(3):8-15,35. Kong J J, Liu H X, Wang X, et al. Impacts of climate change and human activities on vegetation NPP Variation in the Taihang Mountain Area [J]. Shandong Forestry Science and Technology, 2024,54(3):8-15, 35.
[38] 张心茹,曹 茜,季舒平,等.气候变化和人类活动对黄河三角洲植被动态变化的影响 [J]. 环境科学学报, 2022,42(1):56-69. Zhang X R, Cao Q, Ji S P, et al. Impacts of climate change and human activities on vegetation dynamics in the Yellow River Delta [J]. Acta Scientiae Circumstantiae, 2022,42(1):56-69.
[39] 杨 丹,王晓峰.黄土高原气候和人类活动对植被NPP变化的影响 [J]. 干旱区研究, 2022,39(2):584-593. Yang D, Wang X F. Effects of climate and human activities on vegetation NPP Changes in the Loess Plateau [J]. Journal of Arid Land Studies, 2022,39(2):584-593.
[40] 黄乾元.珠三角城市扩张对生态系统碳储量的影响研究 [D]. 深圳:深圳大学, 2022. Huang Q Y. Impact of urban expansion on ecosystem carbon storage in the Pearl River Delta [D]. Shenzhen: Shenzhen University, 2022.
[41] 王艳芳.河南省退耕还林工程固碳成效及其潜力评估 [D]. 杨凌:西北农林科技大学, 2017. Wang Y F. Evaluation of carbon sequestration efficacy and potential under grain for green programme in Henan Province, China [D]. Yangling: Northwest A&F University, 2017.

基金

四川省科技教育联合基金(2025NSFSC2047);四川省重点研发项目(2023YFG0129);四川省科普培训项目(2025JDKP0028);四川海聚计划高端人才引进项目(25RCYJ0053,24RCYJ0018)

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