资源科学 ›› 2017, Vol. 39 ›› Issue (9): 1712-1724.doi: 10.18402/resci.2017.09.10
收稿日期:
2016-12-21
修回日期:
2017-03-02
出版日期:
2017-09-20
发布日期:
2017-09-20
作者简介:
作者简介:李国平,女,四川宜宾人,教授、博士生导师,研究方向为资源环境与生态补偿。E-mail:
基金资助:
Received:
2016-12-21
Revised:
2017-03-02
Online:
2017-09-20
Published:
2017-09-20
摘要:
已有关于生态补偿的研究,较少从宏观视角考虑生态补偿资金规模对区域经济增长的影响。本文在拉姆塞-卡斯-库普曼宏观增长模型的基础上,纳入生态补偿提供资金和生态效益的过程,从理论上分析生态补偿资金投入对经济增长的影响,表明生态补偿资金通过提高资本的增长率进而推动经济增长。再利用陕西省79个退耕还林县的统计数据进行实证研究。首先,采用二步聚类法对陕西省内各县的退耕还林规模加以分类,并以此作为解释变量进行面板回归,分析退耕还林规模对县域经济增长的平均影响,结果表明退耕还林促进县域经济增长,且高退耕还林规模对县域经济增长的促进作用更大。然后,采用分位数回归方法分析在不同经济增长率条件下,退耕还林规模对经济增长的边际影响,结果表明,县域经济增长率越高,退耕还林生态补偿对经济增长的作用越大;无论县域经济增长率如何变化,退耕地还林对县域经济增长的促进作用都大于荒山荒地造林和封山育林。
李国平, 石涵予. 退耕还林生态补偿与县域经济增长的关系分析——基于拉姆塞-卡斯-库普曼宏观增长模型[J]. 资源科学, 2017, 39(9): 1712-1724.
Guoping LI, Hanyu SHI. The relationship between GTGP and regional economic growth based on Ramsey-Cass-Koopmans Modeling[J]. Resources Science, 2017, 39(9): 1712-1724.
表 2
自变量的描述性统计"
变量 | 均值 | 标准差 | 最小值 | 最大值 | 偏度 | 峰度 | Jarque-Bera统计量 |
---|---|---|---|---|---|---|---|
FIN | 0.204 1 | 0.129 3 | 0.035 5 | 0.621 3 | 1.474 1 | 4.952 5 | 40.639 2*** |
SAVE | 0.679 2 | 0.294 3 | 0.086 0 | 2.073 1 | 1.071 6 | 8.040 5 | 97.499 4*** |
SEC | 0.442 8 | 0.231 5 | 0.079 6 | 1.064 6 | 0.833 8 | 2.742 7 | 9.253 7*** |
SCH | 0.077 0 | 0.015 8 | 0.047 0 | 0.109 3 | 0.107 0 | 2.154 6 | 2.472 0 |
FIX | 0.529 4 | 0.250 3 | 0.123 6 | 1.623 9 | 1.803 4 | 7.631 7 | 112.001 6*** |
EM | 13.968 7 | 8.788 5 | 1.090 3 | 41.080 1 | 0.985 1 | 3.911 6 | 15.318 6*** |
URP | 0.833 3 | 0.074 6 | 0.604 9 | 0.983 7 | -1.026 2 | 4.046 5 | 17.249 9*** |
TG | 11 424.47 | 10 907.18 | 0 | 62 653.33 | 2.409 3 | 9.981 4 | 233.864 8*** |
TTG | 27 389.31 | 20 469.13 | 466.666 7 | 123 580.00 | 2.061 1 | 8.581 3 | 156.467 6*** |
表 4
对人均生产总值增长率回归的结果"
变量 | 回归1 | 回归2 | 回归3 | 回归4 | 回归5 | 回归6 |
---|---|---|---|---|---|---|
0.236 2*** | 0.138 9*** | 0.139 0*** | 0.183 9*** | 0.251 1*** | 0.232 8*** | |
FIN | 0.092 1*** | 0.113 1*** | 0.113 0*** | 0.030 7 | -0.028 0 | 0.022 0 |
SAVE | -0.086 2*** | -0.096 4*** | -0.097 1*** | -0.089 7*** | -0.063 4*** | -0.065 6*** |
SEC | 0.118 4*** | 0.101 9*** | 0.101 2*** | 0.090 9*** | 0.109 4*** | 0.105 6*** |
SCH | - | 1.511 4*** | 1.507 0*** | 1.577 6*** | 1.598 0*** | 1.711 7*** |
FIX | - | - | 0.001 6 | 0.003 7 | -0.016 4 | -0.015 6 |
EM | - | - | - | -0.002 0*** | -0.003 4*** | -0.003 2*** |
URP | - | - | - | - | -0.309 3*** | -0.304 4*** |
D | - | - | - | - | - | 0.015 7** |
修R2 | 0.278 9 | 0.302 4 | 0.299 3 | 0.305 0 | 0.334 3 | 0.342 6 |
样本量 | 711 | 711 | 711 | 711 | 711 | 711 |
表 5
分位数回归模型的估计结果"
回归模型 | 解释变量 | 全样本OLS | 0.2分位 | 0.5分位 | 0.7分位 | 0.9分位 |
---|---|---|---|---|---|---|
模型一 | TG | 0.011 0***(0.002 6) | 0.002 9 (0.003 5) | 0.007 2** (0.002 7) | 0.007 4(0.005 5) | 0.030 3***(0.010 1) |
修正的R2 | 0.585 8 | — | — | — | — | |
Pseudo R2 | — | 0.075 7 | 0.236 1 | 0.378 3 | 0.544 2 | |
模型二 | TTG | 0.005 4***(0.001 6) | 0.002 2***(0.001 4) | 0.003 3* (0.001 4) | 0.004 0(0.002 4) | 0.011 8** (0.004 8) |
修正的R2 | 0.560 6 | — | — | — | — | |
Pseudo R2 | — | 0.090 6 | 0.211 0 | 0.353 9 | 0.490 2 |
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