渭河流域水土流失治理效率的时空格局演化与影响因素
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摘要
运用Bootstrap-DEA模型计算2000～2015年渭河流域陕西段39个县区水土流失治理效率;结合探索性空间分析方法(ESDA)和地理加权回归模型(GWR)分析各县水土流失治理效率的时空演化特征及其影响因素。研究结果表明:①2000～2015年,研究区水土流失治理效率从0.263增至0.336,整体治理效率仍处较低水平;前期生态治理效率的增加缘于纯技术效率的提高,后期规模效率对治理效率的增加起到了主导作用。②县域的治理效率在空间上呈现集聚状态;热点分析结果进一步显示,2005年治理效率表现出2个热点集聚区,即以宝鸡市千阳县为热点区、乾县和武功县为核心辐射至永寿县和礼泉县的热点集聚区,冷点区域则以临潼为中心,辐射到西安市辖区、高陵、富平和三原县;2010～2015年治理效率的热点区域与冷点区域较2005年有所收缩且保持相对稳定。③渭河流域水土流失治理效率的时空变化是降水、坡度、灌草面积覆盖度、人口经济和农业生产共同作用的结果,且各影响因素在不同时期对各县的治理效率的贡献呈动态变化,这意味着政策制定者需要从全局角度权衡不同因素的影响效果。
Objectively assessing the effectiveness of ecological restoration measures and analyzing effective ways to promote the efficiency of ecological management are important scientific and policy issues in the Weihe River Basin. Using an interdisciplinary approach, the aim of this study is to measure the control efficiency of the Sloping Land Conversion Program(SLCP)and terrace fields on soil and water loss by adopting the Bootstrap-DEA model and using a comprehensive dataset(including biophysical and socioeconomic data) from 39 counties in the period 2000-2015. Then, exploratory spatial data analysis(ESDA) was used to capture the spatial correlation in overall control efficiency. Finally, geographically weighted regression(GWR) was employed to identify the spatial heterogeneity and evolutionary characteristics in the relationship between control efficiency and natural conditions and socioeconomic development in each sample county. Results show that the control efficiency of soil erosion increased from 0.263 to 0.336 during the study period. The increase of the treatment efficiency for soil and water loss in the early stage was due to the improvement of pure technical efficiency; while later the scale efficiency played a leading role in promoting the treatment efficiency. In addition,the efficiency showed a stable spatial agglomeration. The hotspots of efficiency were concentrated primarily in Baoji City, while the cold-spot center was Lintong, the radius of which extended to the municipality districts of Xi'an, Gaoling, and Fuping. The difference in control efficiency is the result of a combination of multiple factors; the factors affecting control efficiency vary across counties, indicating that regional governments should consider full-scale initiatives.
Objectively assessing the effectiveness of ecological restoration measures and analyzing effective ways to promote the efficiency of ecological management are important scientific and policy issues in the Weihe River Basin. Using an interdisciplinary approach, the aim of this study is to measure the control efficiency of the Sloping Land Conversion Program(SLCP)and terrace fields on soil and water loss by adopting the Bootstrap-DEA model and using a comprehensive dataset(including biophysical and socioeconomic data) from 39 counties in the period 2000-2015. Then, exploratory spatial data analysis(ESDA) was used to capture the spatial correlation in overall control efficiency. Finally, geographically weighted regression(GWR) was employed to identify the spatial heterogeneity and evolutionary characteristics in the relationship between control efficiency and natural conditions and socioeconomic development in each sample county. Results show that the control efficiency of soil erosion increased from 0.263 to 0.336 during the study period. The increase of the treatment efficiency for soil and water loss in the early stage was due to the improvement of pure technical efficiency; while later the scale efficiency played a leading role in promoting the treatment efficiency. In addition,the efficiency showed a stable spatial agglomeration. The hotspots of efficiency were concentrated primarily in Baoji City, while the cold-spot center was Lintong, the radius of which extended to the municipality districts of Xi'an, Gaoling, and Fuping. The difference in control efficiency is the result of a combination of multiple factors; the factors affecting control efficiency vary across counties, indicating that regional governments should consider full-scale initiatives.
引文
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[3] Wang Y F, Yao S B. Effects of restoration practices on controlling soil and water losses in the Wei River Catchment,China:an estimation based on longitudinal field observations[J]. Forest Policy and Economics,2019,100:120-128.
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[6]牛最荣,赵文智,刘进琪,等.甘肃渭河流域土地利用及覆被变化对径流的影响研究[J].水利水电技术, 2012, 43(4):5-10.[Niu Zuirong, Zhao Wenzhi, Liu Jinqi et al. Study on impact from change of land-use and land-cover on runoff in Wei River Basin in Gansu Province. Water Resources and Hydropower Engineering, 2012, 43(4):5-10.]
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[12]武国胜,林惠花,曾宏达.用RS和GIS技术评价福建省长汀县土壤保持功能对生态系统变化的响应[J].生态学报,2017,37(1):321-330.[Wu Guosheng, Lin Huihua, Zeng Hongda. Responses of soil conservation function to ecosystem changes:An assessment based on RS and GIS in Changting County, Fujian Province. Acta Ecologica Sinica,2017, 37(1):321-330.]
[13]王连芬,孙平平.区域环境治理效率测的评价指标体系研究[J].统计与决策, 2012(10):60-62.[Wang Lianfen, Sun Pingping. Study on the evaluation index system of regional environmental governance efficiency test. Statistics and Decision, 2012(10):60-62.]
[14]侯燕.中国生态治理效率及其变动[J].北京理工大学学报(社会科学版), 2015, 17(6):21-29.[Hou Yan. China’s ecological management efficiency and changes. Journal of Beijing Institute of Technology(Social Sciences Edition), 2015, 17(6):21-29.]
[15] Tenge A J, Graaff J D, Hella J P. Financial efficiency of major soil and water conservation measures in West Usambara highlands, Tanzania[J]. Applied Geography, 2005, 25(4):348-366.
[16] Claassen R, Cattaneo A, Johansson R. Cost-effective design of agri-environmental payment programs:U.S. experience in theory and practice[J]. Ecological Economics, 2008, 65(4):737-752.
[17] DumbrovskyM, SobotkováV,?arapatka B et al. Cost-effectiveness evaluation of model design variants of broad-base terrace in soil erosion control[J]. Ecological Engineering, 2014, 68:260-269.
[18] Wang L F, Sun P P. Study on the evaluation index system of regional environmental governance efficiency test[J]. Statistics and Decision,2012(10):60-62.
[19] Liu P, Yin R S, Zhao M J. Reformulating China's ecological restoration policies:What can be learned from comparing Chinese and American experiences[J]? Forest Policy and Economics,2019, 98:54-61.
[20] Yin R S, Zhao M J. Ecological restoration programs and payments for ecosystem services as integrated biophysical and socioeconomic processes—China's experience as an example[J].Ecological Economics, 2012, 73:56-65.
[21] Costanza R, D’Arge R, De Groot R S et al. The value of the world’s ecosystem services and natural capital[J]. Nature,1997,387:253-260.
[22] Leimona B, Van Noordwijk M, De Groot R et al. Fairly efficient, efficiently fair:Lessons from designing and testing payment schemes for ecosystem services in Asia[J]. Ecosystem Services, 2015,12:16-28.
[23]陕西省水利厅.陕西水利统计年鉴[M].西安:陕西科学技术出版社,2001-2016.[Shaanxi Provincial Department of Water Resources. Water conservancy statistical yearbook of Shaanxi. Xi’an:Shaanxi Science and Technology Press,2001-2016.]
[24]中华人民共和国水利部.黄河流域水文资料年鉴(泾渭洛河水系)[M].北京:中国水利水电出版社, 2001-2016.[The Ministry of Water Resources of the People’s Republic of China. Hydrological statistics yearbook of Yellow River Bain. Beijing:China Water Conservancy and Hydropower Press, 2001-2016.]
[25]刘纪远,匡文慧,张增祥,等. 20世纪80年代末以来中国土地利用变化的基本特征与空间格局[J].地理学报,2014,69(1):3-14.[Liu Jiyuan, Kuang Wenhui, Zhang Zengxiang et al. Spatiotemporal characteristics, patterns and causes of land use changes in China since the late 1980s. Acta Geographica Sinica, 2014,69(1):3-14.]
[26] National Climatic Center of the China Meteorological Administration. China’s surface climate data of daily value dataset. 2000-2015(v3.0, http://cdc.nmic.cn/).
[27] ASTER-GDEMV2 30m DEM model. National Aeronautics and Space Administration(search.earthdata.nasa.gov)
[28] Yin R, Yin G. China’s primary programs of terrestrial ecosystem restoration:Initiation, implementation, and challenges[J].Environmental Management, 2010, 45(3):429-441.
[29] Zhang D W. China's forest expansion in the last three plus decades:Why and how?[J]. Forest Policy and Economics, 2019,98:75-81.
[30] Song M L, Zhang L L, Liu W et al. Bootstrap-DEA analysis of BRICS’energy efficiency based on small sample data[J]. Applied Energy, 2013, 112(C):1049-1055.
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