中国土壤侵蚀的社会经济空间驱动因素研究: 基于多尺度地理加权回归模型的分析
作者:
作者单位:

1.华中农业大学资源与环境学院,武汉 430070;2.华中农业大学公共管理学院,武汉 430070;3.华中农业大学国土空间治理与绿色发展研究中心,武汉 430070

作者简介:

栗珂珂,E-mail:KekeLi@webmail.hzau.edu.cn

通讯作者:

王真,E-mail:sinoo@mail.hzau.edu.cn

中图分类号:

K992.2

基金项目:

国家自然科学基金项目(42077060;42377321)


Analyzing socio-economic spatial factors driving soil erosion in China based on multi-scale geographically weighted regression model
Author:
Affiliation:

1.College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China;2.College of Public Administration, Huazhong Agricultural University, Wuhan 430070, China;3.Interdisciplinary Research Center for Territorial Spatial Governance and Green Development, Huazhong Agricultural University, Wuhan 430070, China

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    摘要:

    土壤侵蚀威胁粮食安全和生态系统服务,是中国面临的严峻环境问题之一,同时受到自然因素和人类活动的共同影响。目前国内已有大量研究关注社会经济因素对土壤侵蚀的驱动作用,但关于两者之间空间非平稳关系的探讨和影响因素作用尺度差异性的关注仍存在不足。为探究社会经济活动对土壤侵蚀的复杂驱动机制,以中国346个地级市为研究对象,以2017年为参考年,基于修正的通用土壤流失方程(RUSLE)和多尺度地理加权回归(MGWR)模型,揭示中国各地级市土壤侵蚀的空间异质性,并探索社会经济因素对中国各地级市土壤侵蚀速率的空间驱动作用及因素间的作用尺度差异。研究显示:中国各地级市土壤侵蚀速率的空间分布具有明显的空间正相关性,侵蚀热点主要分布在西部地区、东北地区、云贵高原和四川盆地以及黄土高原;与基于全局回归的模型及传统的地理加权回归模型相比,MGWR可以大大提高社会经济变量对土壤侵蚀速率的解释程度,模型拟合优度达到0.87;从驱动因素来看,除人均地区生产总值外,各驱动因素对中国各地级市土壤侵蚀速率的影响方向会随着空间位置变化产生结构性差异;平均而言,人口密度是对中国各地级市土壤侵蚀速率贡献最大的因素;中国地级市土壤侵蚀速率在西部地区更容易受到复种指数的影响,在东部地区社会经济因素对土壤侵蚀速率的驱动机制更为复杂,不同驱动因素作用的空间尺度差异更明显。研究表明,决策者应充分考虑人类活动对土壤侵蚀影响的空间异质性,以实现水土保持的可持续发展。

    Abstract:

    Soil erosion poses a threat to food security and ecosystem services, and is one of the severe environmental problems facing China. It is affected by both natural factors and human activities. At present, a large number of studies in China have focused on the driving effect of socio-economic factors on soil erosion, but there are still insufficient in studying the spatial non-stationary relationship between the two and paying attention to the scale differences of affecting factors. 346 prefecture-level cities in China were used to study the complex driving mechanisms of socio-economic activities on soil erosion. The soil erosion prediction (RUSLE) model and multiscale geographically weighted regression (MGWR) model were used to investigate the spatial heterogeneity of soil erosion in various cities in China using 2017 as a reference year. The spatial driving effects of socio-economic factors on rates of soil erosion in prefecture-level cities across China and the differences in the scale of action between factors were studied. Results showed that the spatial distribution of the rate of soil erosion in various prefecture-level cities in China had a significant positive spatial correlation. Hotspots of soil erosion were mainly distributed in the west, northeast, the Yunnan-Guizhou Plateau, Sichuan Basin and the Loess Plateau. Compared with models based on global regression and traditional geographically weighted regression models, MGWR significantly improved the explanatory power of socio-economic variables on the rate of soil erosion, with a model fitting goodness of 0.87. From the perspective of driving factors, the direction of the effect of each driving factor on the rate of soil erosion in various prefecture-level cities in China had structural differences with changes of spatial location except for the factor of regional gross domestic product per capita. On average, population density was the factor contributing the most to the rate of soil erosion in various prefecture-level cities in China. The rate of soil erosion in prefecture level cities in China was more susceptible to the influence of multiple cropping indices in the western region, while the driving mechanism of socio-economic factors on the rate of soil erosion in the eastern region was more complex, and the spatial scale differences of different driving factors were more obvious. It is indicated that decision makers should fully consider the spatial heterogeneity of the impact of human activities on soil erosion to achieve the sustainable development of soil and water conservation.

    图1 中国土壤侵蚀现状的空间分布特征Fig.1 The spatial distribution characteristics of soil erosion in China
    图2 多尺度地理加权回归模型中决定系数(A)和标准化残差的空间分布(B)Fig.2 The spatial distribution of local R2(A) and standardized residual in MGWR model(B)
    图3 多尺度地理加权回归模型中各社会经济因素回归系数的空间分布Fig.3 Spatial distribution of regression coefficients of variables in the MGWR model
    表 1 本研究采用数据来源Table 1 Data sources of this study
    参考文献
    [1] MONTGOMERY D R.Soil erosion and agricultural sustainability[J].PNAS,2007,104(33):13268-13272.
    [2] QUINE T A,VAN OOST K.Insights into the future of soil erosion[J].PNAS,2020,117(38):23205-23207.
    [3] 张启旺,安俊珍,王霞,等.中国土壤侵蚀相关模型研究进展[J].中国水土保持,2014(1):43-46. ZHANG Q W,AN J Z,WANG X.Progress of study on China soil erosion correlation models[J].Soil and water conservation in China,2014(1):43-46(in Chinese with English abstract).
    [4] ANANDA J,HERATH G.Soil erosion in developing countries:a socio-economic appraisal[J].Journal of environmental management,2003,68(4):343-353.
    [5] STOCKING M A,MURNAGHAN N.A Handbook for the field assessment of land degradation[M].London:Routledge,2002.
    [6] LI K K,WANG L,WANG Z,et al.Multiple perspective accountings of cropland soil erosion in China reveal its complex connection with socioeconomic activities[J/OL].Agriculture,ecosystems & environment,2022,337:108083[2024-02-20].https://doi.org/10.1016/j.agee.2022.108083.
    [7] WANG L,YAN H,WANG X W,et al.The potential for soil erosion control associated with socio-economic development in the hilly red soil region,Southern China[J/OL].CATENA,2020,194:104678[2024-02-20].https://doi.org/10.1016/j.catena.2020.104678.
    [8] WANG B,ZENG Y,LI M J,et al.Evaluation of the driving effects of socio-economic development on soil erosion from the perspective of prefecture-level[J/OL].Frontiers in environmental science,2022,10:1066889[2024-02-20].https://doi.org/10.3389/fenvs.2022.1066889.
    [9] 王刚,张秋平,郑海金,等.1987—2013年江西省水土流失趋势及其社会经济驱动力分析[J].生态科学,2017,36(3):115-120.WANG G,ZHANG Q P,ZHENG H J,et al.Temporal variation of soil and water loss and its social-economic driving forces in Jiangxi Province from 1987 to 2013[J].Ecological science,2017,36(3):115-120(in Chinese with English abstract).
    [10] WUEPPER D,BORRELLI P,FINGER R.Countries and the global rate of soil erosion[J].Nature sustainability,2020,3:51-55.
    [11] YU S X,XIE C Y,ZHAO J S,et al.Socioeconomic development mitigates runoff and sediment yields in a subtropical agricultural watershed in Southern China[J/OL].Environmental research letters,2021:024053[2024-02-20].https://doi.org/10.1088/1748-9326/abdd5a.
    [12] WANG Z, ZENG Y, LI C, et al. Telecoupling cropland soil erosion with distant drivers within China[J/OL]. Journal of environmental management,2021,288:112395[2024-02-20].https://doi.org/10.1016/j.jenvman.2021.112395.
    [13] CUI H W,WANG Z,YAN H,et al.Production-based and consumption-based accounting of global cropland soil erosion[J].Environmental science & technology,2022,56(14):10465-10473.
    [14] ISTANBULY M N,KRáSA J,JABBARIAN AMIRI B.How socio-economic drivers explain landscape soil erosion regulation services in Polish Catchments[J/OL].International journal of environmental research and public health,2022,19(4):2372[2024-02-20].https://doi.org/10.3390/ijerph19042372.
    [15] BHANDARI K P,ARYAL J,DARNSAWASDI R.A geospatial approach to assessing soil erosion in a watershed by integrating socio-economic determinants and the RUSLE model[J].Natural hazards,2015,75(1):321-342.
    [16] LIGONJA P J,SHRESTHA R P.Soil erosion assessment in Kondoa eroded area in Tanzania using universal soil loss equation,geographic information systems and socioeconomic approach[J].Land degradation & development,2015,26(4):367-379.
    [17] HALIM R,CLEMENTE R S,ROUTRAY J K,et al.Integration of biophysical and socio-economic factors to assess soil erosion hazard in the Upper Kaligarang Watershed,Indonesia[J].Land degradation & development,2007,18(4):453-469.
    [18] 祝新明,宋小宁,冷佩,等.多尺度地理加权回归的地表温度降尺度研究[J].遥感学报,2021,25(8):1749-1766.ZHU X M,SONG X N,LENG P,et al.Spatial downscaling of land surface temperature with the multi-scale geographically weighted regression[J].National remote sensing bulletin,2021,25(8):1749-1766(in Chinese with English abstract).
    [19] 沈体雁,于瀚辰,周麟,等.北京市二手住宅价格影响机制:基于多尺度地理加权回归模型(MGWR)的研究[J].经济地理,2020,40(3):75-83.SHEN T Y,YU H C,ZHOU L,et al.On hedonic price of second-hand houses in Beijing based on multi-scale geographically weighted regression:scale law of spatial heterogeneity[J].Economic geography,2020,40(3):75-83(in Chinese with English abstract).
    [20] 乔磊,张吴平,黄明镜,等.基于MGWRK的土壤有机质制图及驱动因素研究[J].中国农业科学,2020,53(9):1830-1844.QIAO L,ZHANG W P,HUANG M J,et al.Mapping of soil organic matter and its driving factors study based on MGWRK[J].Scientia agricultura sinica,2020,53(9):1830-1844(in Chinese with English abstract).
    [21] RENARD K G,FOSTER G R,WEESIES G A,et al.Predicting soil erosion by water:a guide to conservation planning with the revised universal soil loss equation (RUSLE)[M]. New York:US Department of Agriculture,Agricultural Research Service,1997.
    [22] 李佳蕾,孙然好,熊木齐,等.基于RUSLE模型的中国土壤水蚀时空规律研究[J].生态学报,2020,40(10):3473-3485.LI J L,SUN R H,XIONG M Q,et al.Estimation of soil erosion based on the RUSLE model in China[J].Acta ecologica sinica,2020,40(10):3473-3485(in Chinese with English abstract).
    [23] BORRELLI P,ROBINSON D A,FLEISCHER L R,et al.An assessment of the global impact of 21st century land use change on soil erosion[J/OL].Nature communications,2017,8(1):2013[2024-02-20].https://doi.org/10.1038/s41467-017-02142-7.
    [24] XIONG M Q,SUN R H,CHEN L D.Global analysis of support practices in USLE-based soil erosion modeling[J].Progress in physical geography:earth and environment,2019,43(3):391-409.
    [25] MORAN P A P.Notes on continuous stochastic phenomena[J].Biometrika,1950,37(1/2):17-23.
    [26] FOTHERINGHAM A S,BRUNSDON C,CHARLTON M.Quantitative geography:perspectives on spatial data analysis[M].London:Sage Publications,2000.
    [27] FOTHERINGHAM A S,YANG W B,KANG W.Multiscale geographically weighted regression (MGWR)[J/OL].Annals of the American Association of Geographers,2017,107(6):1247-1265[2024-02-20].https://doi.org/10.1080/24694452.2017.1352480.
    [28] YU H C,FOTHERINGHAM A S,LI Z Q,et al.Inference in multiscale geographically weighted regression[J].Geographical analysis,2020,52(1):87-106.
    [29] HASTIE T J.Generalized additive models[M].London:Statistical Models in Routledge,2017:249-307.
    [30] SONDEREGGER T,PFISTER S.Global assessment of agricultural productivity losses from soil compaction and water erosion[J].Environmental science & technology,2021,55(18):12162-12171.
    [31] CAO B W,YU L,NAIPAL V,et al.A 30 m terrace mapping in China using Landsat 8 imagery and digital elevation model based on the Google Earth Engine[J].Earth system science data,2021,13(5):2437-2456.
    [32] 何琴.跨省流域生态补偿法律制度探究:以赤水河流域治理为例[J].四川环境,2023,42(3):249-256.HE Q.Research on the legal system of ecological compensation in cross-provincial watershed:taking the Chishui River Basin management as an example[J].Sichuan environment,2023,42(3):249-256(in Chinese with English abstract).
    [33] KUANG W H,LIU J Y,TIAN H Q,et al.Cropland redistribution to marginal lands undermines environmental sustainability[J/OL].National science review,2022,9(1):nwab091[2024-02-20].https://doi.org/10.1093/nsr/nwab091.
    [34] KONG X B.China must protect high-quality arable land[J/OL].Nature,2014,506(7486):7[2024-02-20].https://doi.org/10.1038/506007a.
    [35] DUAN J K,REN C C,WANG S T,et al.Consolidation of agricultural land can contribute to agricultural sustainability in China[J].Nature food,2021,2:1014-1022.
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栗珂珂,周詹杭,王真.中国土壤侵蚀的社会经济空间驱动因素研究: 基于多尺度地理加权回归模型的分析[J].华中农业大学学报,2024,43(6):29-38

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  • 收稿日期:2024-02-20
  • 在线发布日期: 2025-01-07
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