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地理科学进展  2015, Vol. 34 Issue (6): 665-675    DOI: 10.18306/dlkxjz.2015.06.002
  城市与交通地理 本期目录 | 过刊浏览 | 高级检索 |
中国集中连片特困地区公路交通优势度及其对经济增长的影响
王武林1,杨文越1,曹小曙1,2,*()
1. 中山大学地理科学与规划学院,广州 510275
2. 陕西师范大学交通地理与空间规划研究所,西安 710062
Road transport superiority degree and impact on economic growth in the concentrated contiguous severe poverty areas in China
Wulin WANG1,Wenyue YANG1,Xiaoshu CAO1,2,*()
1. School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
2. Institute of Transport Geography and Spatial Planning, Shaanxi Normal University, Xi'an 710062, China
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摘要 

通过集成公路网络密度、城市邻近度和加权平均出行时间等指标,测量了2012年中国14个集中连片特困地区680个县(市、区)的公路交通优势度,对其空间格局进行综合评价,并通过选择拟合程度最优的SEM模型探讨了公路交通优势度对经济增长的影响。研究发现:①14个集中连片特困地区680个县(市、区)的公路网络密度、城市邻近度和加权平均出行时间的差异较大,公路交通优势度呈现偏右的正态分布;②将公路交通优势度从优到劣分为4个等级,第一等级为大别山区、罗霄山区、吕梁山区、燕山—太行山区;第二等级为秦巴山区、六盘山区、武陵山区、乌蒙山区、滇桂黔石漠化区;第三等级为滇西边境山区、大兴安岭南麓山区以及四省藏区;第四等级为西藏区和新疆南疆三地州;③SEM模型的拟合优度高于OLS模型和SLM模型,结果显示,公路交通优势度对经济增长具有较明显的推动作用,公路交通优势度每提升1个百分点,对其经济产出的贡献达到0.193%,提高公路网络密度能提升公路交通优势度,进而促进当地经济增长。

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关键词 公路交通优势度集中连片特困地区空间格局影响中国    
Abstract

This research measured road transport superiority degree of the 680 counties (county-level cities and districts) in the 14 concentrated contiguous severe poverty areas of China in 2012 by integrating the indices of road network density, city proximity, and weighted average travel time, and made a comprehensive evaluation of their spatial patterns. It then chose the optimal model of SEM to explore the impact of road transport superiority degree on economic growth. The results show that: (1) With regard to road network density, city proximity, and weighted average travel time, there is a large difference among the 680 counties (county-level cities and districts) in the 14 concentrated contiguous severe poverty areas. Road transport superiority degree of the 680 counties (county-level cities and districts) presents a right-skewed normal distribution. (2) Road transport superiority degree is divided into four classes from good to bad. The first (best) class includes the Dabie Mountain area, Luoxiao Mountain area, Lvliang Mountain area, and Yanshan-Taihang Mountain area; the second class includes the Qinba Mountain area, Liupan Mountain area, Wuling Mountain area, Wumeng Mountain area, Dian-Gui-Qian Rocky Desertification area; the third class contains the western Yunnan border mountain areas, the Great Khingan South Mountain area, and the Tibetan area in four provinces; the rest of the 14 concentrated contiguous severe poverty areas belong to the fourth (worse) class, which includes Tibet and three prefectures of southern Xinjiang. (3) The fitness of the SEM model is superior compared to the OLS model and the SLM model. The impact of road transport superiority degree on economic growth is clearly significant: every increase of one percentage point in road transport superiority degree has a contribution of 0.193% on economic growth. Therefore improving road network density can enhance road transport superiority degree and promote local and regional economic growth.

Key wordsroad transport superiority degree    concentrated contiguous severe poverty area    spatial pattern    impacts    China
     出版日期: 2015-07-17
基金资助:中央高校基本科研业务费专项资金项目(GK201303006);国家自然科学基金项目(41171139,41130747)
通讯作者: 曹小曙     E-mail: caoxsh@mail.sysu.edu.cn
引用本文:   
王武林,杨文越,曹小曙. 中国集中连片特困地区公路交通优势度及其对经济增长的影响[J]. 地理科学进展, 2015, 34(6): 665-675.
Wulin WANG,Wenyue YANG,Xiaoshu CAO. Road transport superiority degree and impact on economic growth in the concentrated contiguous severe poverty areas in China. PROGRESS IN GEOGRAPHY, 2015, 34(6): 665-675.
链接本文:  
http://www.progressingeography.com/CN/10.18306/dlkxjz.2015.06.002      或      http://www.progressingeography.com/CN/Y2015/V34/I6/665
编号 名称 面积/104km2 省级行政单位 地级行政单位/个 县级行政单位/个 GDP/亿元 人口/万
1 六盘山区 15.30 陕、甘、青、宁 15 61 2564 2090
2 秦巴山区 21.83 豫、鄂、渝、川、陕、甘 18 75 5256 3419
3 武陵山区 16.32 鄂、湘、渝、黔 12 64 4439 3368
4 乌蒙山区 10.70 川、贵、云 10 38 2428 2349
5 滇桂黔石漠化区 21.28 桂、贵、云 15 80 3377 2887
6 滇西边境山区 19.21 10 56 2131 1448
7 大兴安岭南麓山区 11.18 内蒙、吉、黑 4 19 1173 705
8 燕山—太行山区 9.27 冀、晋、内蒙 6 33 1779 1101
9 吕梁山区 3.63 晋、陕 4 20 561 410
10 大别山区 6.47 皖、豫、鄂 11 36 4370 3614
11 罗霄山区 5.21 赣、湘 6 23 1528 1115
12 西藏区 122.84 7 74 750 290
13 四省藏区 99.62 云、川、甘、青 12 77 1445 542
14 新疆南疆三地州 43.65 3 24 725 684
14个片区合计 406.51 21 124* 680 32526 24022
占全国比重/% 42.34 61.76 37.24 23.81 6.26 17.74
Tab.1  2012年14个片区概况
Fig.1  区域公路交通优势度评价指标体系
等级 城市 权重
1 主要城市:4个直辖市,27个省会(首府)城市,非省会的副省级城市5个 0.65
2 一般城市:300个地级市 0.35
Tab.2  中国地级市以上城市分类及权重设定
Fig.2  2012年14个片区680个县(市、区)公路网络密度
片区 省道以上公路长度/km 公路网络密度/(km/100 km2) 片区 省道以上公路长度/km 公路网络密度/(km/100 km2)
六盘山区 8378.12 5.41 燕山—太行山区 6451.02 7.32
秦巴山区 12837.25 5.88 吕梁山区 2363.89 6.88
武陵山区 9481.54 5.58 大别山区 6639.32 9.74
乌蒙山区 4916.35 4.52 罗霄山区 4095.87 7.49
滇桂黔石漠化区 9897.77 4.57 西藏区 14279.51 1.20
滇西边境山区 6272.52 3.10 四省藏区 21301.26 2.19
大兴安岭南麓山区 3324.21 3.11 新疆南疆三地州 4808.24 1.18
Tab.3  2012年14个片区公路网络密度
片区 城市邻近度区间/h 均值/h 标准差/h 片区 城市邻近度区间/h 均值/h 标准差/h
六盘山区 1740~2360 2050 158.61 燕山—太行山区 1817~2376 2035 150.08
秦巴山区 1611~2213 1855 148.85 吕梁山区 1727~1952 1867 58.37
武陵山区 1716~2260 2013 119.10 大别山区 1612~1841 1683 47.57
乌蒙山区 2056~2715 2393 152.44 罗霄山区 1932~2276 2048 85.21
滇桂黔石漠化区 2067~3130 2436 262.15 西藏区 3111~7086 4859 875.63
滇西边境山区 2557~3769 3084 238.65 四省藏区 2168~3840 2811 423.90
大兴安岭南麓山区 2878~3657 3249 186.19 新疆南疆三地州 5331~6182 5675 173.99
Tab.4  2012年14个片区城市邻近度
Fig.3  2012年中国县级城镇经济加权平均出行时间和人口加权平均出行时间
片区 0.0309~0.2311 0.2312~0.4314 0.4315~0.6316 0.6317~0.8319 0.8320~1.0322 1.0323~1.2324 1.2325~1.4327 1.4328~1.6329
六盘山区 0 0 0 0 12 41 7 1
秦巴山区 0 0 0 0 12 50 10 3
武陵山区 0 0 0 0 8 49 7 0
乌蒙山区 0 0 0 2 23 13 0 0
滇桂黔石漠化区 0 0 0 3 46 24 6 1
滇西边境山区 0 0 0 32 24 0 0 0
大兴安岭南麓山区 0 0 0 8 11 0 0 0
燕山—太行山区 0 0 0 0 5 10 14 4
吕梁山区 0 0 0 0 1 9 10 0
大别山区 0 0 0 0 0 6 20 10
罗霄山区 0 0 0 0 0 15 8 0
西藏区 7 20 35 12 0 0 0 0
四省藏区 0 0 2 33 41 1 0 0
新疆南疆三地州 1 20 2 1 0 0 0 0
合计 8 40 39 91 183 218 82 19
Tab.5  14个片区公路交通优势度系数分级与计数
Fig.4  2012年14个片区公路交通优势度
项目 普通最小二乘法(OLS) 空间滞后模型(SLM) 空间误差模型(SEM)
ρ - 0.139* -
a -4.166* -4.042* -4.241*
a1 0.112* 0.101* 0.070*
a2 0.136* 0.132* 0.133*
a3 0.208* 0.149* 0.193*
β1 0.194* 0.173* 0.183*
β2 0.032* 0.023* 0.028*
β3 0.350* 0.337* 0.408*
β4 0.049* 0.035* 0.050*
λ - - 0.446*
R2 0.886* 0.892 0.908
P 0.000 0.000 0.000
Log-Likelihood -298.126 -284.983 -250.501
AIC 612.252 587.965 517.002
SC 648.239 628.45 552.988
Tab.6  不同模型的估计结果
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