资源科学 ›› 2019, Vol. 41 ›› Issue (12): 2274-2283.doi: 10.18402/resci.2019.12.11
收稿日期:
2019-05-21
修回日期:
2019-08-11
出版日期:
2019-12-25
发布日期:
2019-12-25
通讯作者:
陈志刚
作者简介:
王青,女,江苏仪征人,博士,副教授,研究方向为资源管理与公共政策。E-mail: wangqing@njupt.edu.cn
基金资助:
Received:
2019-05-21
Revised:
2019-08-11
Online:
2019-12-25
Published:
2019-12-25
Contact:
CHEN Zhigang
摘要:
城乡差距对于农村土地利用特别是农地违法非农化有着不可忽视的影响。本文首先从理论层面探讨了城乡差距对农地违法非农化的影响机理;在此基础上通过构建计量模型,并运用1999—2016年中国大陆31个省(市、区)的面板数据,进行了系统的实证检验。结果显示:城乡差距扩大会导致农地违法非农化规模显著增长,尤其是随着财政支农支出占比的下降和城乡居民人均可支配收入差距的扩大,农地违法非农化规模增幅明显。具体地,在省级层面,财政支农支出占比每下降一个百分点,农地违法非农化规模会增加近25 hm 2;城乡居民人均可支配收入差距每扩大10%,农地违法非农化规模也会增加约15 hm 2;而城乡土地产出效益差的扩大对农地违法非农化的影响则小得多。此外,在不同经济发展地区(或不同经济发展阶段),城乡差距变化对农地违法非农化的影响存在显著差异。最后从有效遏制农地违法非农化的角度,针对性地提出了缩小城乡差距的相关路径和建议。
王青, 陈志刚. 中国城乡差距对农地违法非农化的影响[J]. 资源科学, 2019, 41(12): 2274-2283.
WANG Qing, CHEN Zhigang. Impact of urban-rural gap on illegal farmland conversion in China[J]. Resources Science, 2019, 41(12): 2274-2283.
表1
模型中各变量的描述统计值"
变量 | 样本量 | 平均值 | 标准差 | 最小值 | 最大值 |
---|---|---|---|---|---|
IFC | 558 | 365.976 | 602.516 | 0.000 | 5561.240 |
INV | 558 | 7223.037 | 8733.038 | 53.557 | 53322.938 |
DPEO | 558 | 26.348 | 111.936 | -859.000 | 1372.000 |
LAND | 558 | 1678.517 | 1004.334 | 82.421 | 4436.117 |
PLC | 558 | 0.556 | 0.497 | 0.000 | 1.000 |
AOF | 558 | 9.180 | 3.368 | 2.133 | 18.966 |
DLE | 558 | 300.732 | 861.046 | 14.280 | 6369.370 |
DUR | 558 | 2.936 | 0.636 | 1.845 | 5.605 |
表2
全国范围模型估计结果"
IFC | 模型1 | 模型2 | 模型3 | 模型4 | 模型5 | 模型6 |
---|---|---|---|---|---|---|
C | -367.213 (261.276) | -268.495 (243.622) | -359.419 (261.832) | 1193.541*** (338.435) | -319.962 (278.789) | 1011.746*** (343.499) |
INV | — | 0.022*** (0.007) | — | — | — | 0.025*** (0.009) |
INV 2 | — | -1.14e-06*** (1.79e-07) | — | — | — | -1.08e-06*** (1.93e-07) |
DPEO | — | — | -0.108 (0.196) | — | — | -0.095 (0.178) |
LAND | — | — | — | -11.157*** (1.627) | — | -8.599*** (1.645) |
PLC | — | — | — | — | 33.308 (68.194) | 56.368 (75.060) |
AOF | -26.069*** (8.746) | -18.485* (10.744) | -26.081*** (8.752) | -13.042 (8.598) | -31.457** (14.083) | -24.395* (12.891) |
DLE | 0.219*** (0.093) | 0.168** (0.087) | 0.218** (0.093) | 0.134 (0.090) | 0.214** (0.093) | 0.108 (0.087) |
DUR | 308.816*** (74.460) | 252.216*** (69.983) | 307.244*** (74.564) | 170.406** (74.188) | 303.760*** (75.230) | 149.617** (72.187) |
N | 558 | 558 | 558 | 558 | 558 | 558 |
R2 | 0.063 | 0.190 | 0.064 | 0.140 | 0.063 | 0.233 |
F | 11.73 | 24.56 | 8.86 | 21.33 | 8.84 | 19.69 |
表3
不同经济发展地区模型估计结果"
IFC | 发达地区 | 欠发达地区 | |||
---|---|---|---|---|---|
估计系数 | 标准差 | 估计系数 | 标准差 | ||
C | 1065.230** | 539.474 | 770.128* | 469.130 | |
INV | 0.047*** | 0.015 | 0.024* | 0.013 | |
INV 2 | -1.37e-06*** | 2.81e-07 | -1.25e-06*** | 3.89e-07 | |
DPEO | -0.074 | 0.239 | -0.043 | 0.280 | |
LAND | -8.455*** | 2.143 | -8.375*** | 2.802 | |
PLC | 33.110 | 120.522 | 42.321 | 95.916 | |
AOF | -82.786*** | 25.276 | -6.171 | 14.577 | |
DLE | -0.276 | 0.597 | 0.065 | 0.080 | |
DUR | 358.938** | 153.069 | 102.483 | 78.485 | |
N | 270 | 288 | |||
R2 | 0.299 | 0.173 | |||
F | 13.15 | 6.90 |
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