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黄河流域山西矿区自然资本占用动态评估及驱动机制

蒋毓琪 杨怡康 朱少英

蒋毓琪,杨怡康,朱少英.黄河流域山西矿区自然资本占用动态评估及驱动机制[J].环境工程技术学报,2022,12(4):1264-1271 doi: 10.12153/j.issn.1674-991X.20210327
引用本文: 蒋毓琪,杨怡康,朱少英.黄河流域山西矿区自然资本占用动态评估及驱动机制[J].环境工程技术学报,2022,12(4):1264-1271 doi: 10.12153/j.issn.1674-991X.20210327
JIANG Y Q,YANG Y K,ZHU S Y.Dynamic evaluation and driving mechanism of natural capital occupation in Shanxi mining areas of the Yellow River Basin[J].Journal of Environmental Engineering Technology,2022,12(4):1264-1271 doi: 10.12153/j.issn.1674-991X.20210327
Citation: JIANG Y Q,YANG Y K,ZHU S Y.Dynamic evaluation and driving mechanism of natural capital occupation in Shanxi mining areas of the Yellow River Basin[J].Journal of Environmental Engineering Technology,2022,12(4):1264-1271 doi: 10.12153/j.issn.1674-991X.20210327

黄河流域山西矿区自然资本占用动态评估及驱动机制

doi: 10.12153/j.issn.1674-991X.20210327
基金项目: 教育部人文社会科学研究规划青年基金项目(20YJC630052);山西省哲学社会科学规划课题(2019B292)
详细信息
    作者简介:

    蒋毓琪(1983—),男,副教授,博士,长期从事资源与环境经济研究,jiangyuqi1913@163.com

    通讯作者:

    杨怡康(1990—),女,硕士研究生,主要研究方向为资源与环境经济,yangyikang1433@163.com

  • 中图分类号: X321

Dynamic evaluation and driving mechanism of natural capital occupation in Shanxi mining areas of the Yellow River Basin

  • 摘要:

    自然资本是经济社会绿色可持续发展的必要保障,对其进行评估是维持区域生态系统平衡的重要内容。以矿区自然资本为视角,运用三维生态足迹模型,测算黄河流域山西矿区生态足迹深度与广度并分析其变化特征,进一步阐释矿区生态占用动态演变的作用机理。结果表明:黄河流域山西矿区人均生态赤字由2010年的4.40 hm2/人增至2016年的6.31 hm2/人,随后降至2019年的5.02 hm2/人,生态足迹与生态承载力的差距总体趋于缩小。2010—2019年,人均生态足迹广度大致呈递减趋势,由0.63 hm2/人降到0.47 hm2/人,而人均生态足迹深度呈现出先增后减的倒U型变化趋势。黄河流域山西矿区自然资本时空动态演变过程的驱动机制是由生态环境供给、资源禀赋、社会发展、经济增长、能源损耗以及煤炭污染等诸多因素共同作用的结果。其中,煤炭污染、能源损耗、资源禀赋和经济增长中的第二产业增加值与矿区自然资本占用动态演变呈正相关,生态环境供给、社会发展中的环境规制指数与其呈负相关。总之,黄河流域山西矿区生态足迹与生态承载力的差距趋于缩小,但生态占用赤字仍然存在。

     

  • 图  1  山西矿区煤田分布

    Figure  1.  Coal resource distribution in Shanxi mining areas

    图  2  自然资本广度与深度的物理意义[22]

    Figure  2.  Physical meanings of depth and size of natural capital

    图  3  2010—2019年黄河流域山西矿区人均生态足迹与人均生态承载力变化趋势

    Figure  3.  Change trend between per capita ecological footprint and per capita ecological carrying capacity of Shanxi mining areas in the Yellow River Basin from 2010 to 2019

    图  4  2010—2019年黄河流域山西矿区存量流量利用比

    Figure  4.  Utilization ratio of stock flows of Shanxi mining areas in the Yellow River Basin from 2010 to 2019

    图  5  黄河流域山西矿区生态占用演化的作用机理

    Figure  5.  Action mechanism of ecological occupation evolution of Shanxi mining areas in the Yellow River Basin

    表  1  2019年黄河流域山西矿区不同土地利用类型面积

    Table  1.   Area of different land types in Shanxi mining areas of the Yellow River Basin in 2019 万hm2

    土地
    利用类型
    西山
    矿区
    大同
    矿区
    沁水
    矿区
    宁武
    矿区
    霍西
    矿区
    河东
    矿区
    均衡
    因子
    产量
    因子
    耕地0.237.1268.574.7620.1943.282.801.66
    草地6.883.0029.357.4413.2032.100.500.91
    林地11.035.90180.2417.6224.7761.341.100.19
    水域0.220.211.490.160.370.620.201.00
    建筑
    用地
    0.180.6211.070.371.686.212.800.60
    化石能
    源用地
    0.240.9614.280.522.358.911.111.00
    总计18.7817.81305.0030.8762.56152.46
    下载: 导出CSV

    表  2  黄河流域山西矿区自然资本占用动态演变的驱动因素

    Table  2.   Driving factors of the dynamic evolution for ecological capital occupation in Shanxi mining areas of the Yellow River Basin

    驱动因素指标自变量
    生态环境供给生态承载力X1
    资源禀赋煤炭开采规模X2
    社会发展城镇化率X3
    人口总数X4
    城镇居民人均可支配收入X5
    农民人均可支配收入X6
    环境规制综合指数[27-28]X7
    经济增长第一产业增加值X8
    第二产业增加值X9
    第三产业增加值X10
    原煤产值在GDP中的占比X11
    能源损耗单位GDP能耗X12
    煤炭污染工业废水排放量X13
    工业二氧化硫排放量X14
    下载: 导出CSV

    表  3  2010—2019年黄河流域山西矿区人均生态足迹深度与广度

    Table  3.   Per capita ecological footprint depth and size of Shanxi mining areas in the Yellow River Basin from 2010 to 2019 hm2/人 

    年份人均生态赤字人均生态足迹广度人均生态足迹深度
    20104.400.637.98
    20114.800.618.87
    20125.220.5610.32
    20135.540.5511.07
    20145.710.5311.77
    20155.900.4913.04
    20166.310.4415.34
    20175.700.4413.95
    20185.420.4612.78
    20195.020.4711.68
    下载: 导出CSV

    表  4  2010—2019年黄河流域山西矿区足迹深度与足迹广度剪刀差

    Table  4.   Scissors difference between footprint depth and footprint size of Shanxi mining areas in the Yellow River Basin from 2010 to 2019

    年份2010201120122013201420152016201720182019
    剪刀差1.131.251.381.431.471.511.56−1.53−1.50−1.48
    下载: 导出CSV

    表  5  PLS模型回归结果

    Table  5.   Regression results of PLS model

    因变量常数项驱动因子
    Y4.753X1X2X3X4X5X6X7
    −0.1650.1470.0830.0620.0270.009−0.110
    X8X9X10X11X12X13X14
    0.0070.1080.0130.1370.1280.1740.182
    下载: 导出CSV
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