地理科学 ›› 2020, Vol. 40 ›› Issue (3): 364-373.doi: 10.13249/j.cnki.sgs.2020.03.004
收稿日期:
2019-01-24
修回日期:
2019-04-11
出版日期:
2020-03-10
发布日期:
2020-05-13
通讯作者:
崔远政
E-mail:ryancuiyz@zufe.edu.cn
作者简介:
姜磊(1983-),男,山东烟台人,博士,副教授,主要研究方向为环境经济学与空间计量分析。E-mail: lei_jiang@zufe.edu.cn
基金资助:
Jiang Lei1, He Shixiong1, Cui Yuanzheng2()
Received:
2019-01-24
Revised:
2019-04-11
Online:
2020-03-10
Published:
2020-05-13
Contact:
Cui Yuanzheng
E-mail:ryancuiyz@zufe.edu.cn
Supported by:
摘要:
基于卫星观测技术核算出中国26个省份2007-2016年的氮氧化物排放量数据。首先,对省域氮氧化物排放的时空分布特征进行了详细分析。然后,采用空间面板数据模型对影响氮氧化物排放的社会经济驱动因素进行了实证研究。结果表明:① 以重工业发展为主的河北、山西、山东、河南、内蒙古以及经济较为发达的江苏和广东是氮氧化物污染的重灾区。② 中国省域氮氧化物排放存在显著的空间正自相关现象。③ 空间计量模型估计结果显示:氮氧化物排放存在显著的空间溢出效应。地区经济发展水平提高、第二产业比重提升以及煤炭消费量增加是引起氮氧化物排放的重要驱动因素。然而,地区技术水平升级以及外商直接投资则可以有效地减少氮氧化物排放,改善环境质量。
中图分类号:
姜磊, 何世雄, 崔远政. 基于空间计量模型的氮氧化物排放驱动因素分析:基于卫星观测数据[J]. 地理科学, 2020, 40(3): 364-373.
Jiang Lei, He Shixiong, Cui Yuanzheng. Analysis of Driving Factors of Nitrogen Oxides Emissions in China Based on Spatial Econometric Models: Data from Satellite Observations[J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(3): 364-373.
表1
传统面板数据回归结果"
变量 | 卫星观测核算数据 | 统计年鉴数据 | |||
---|---|---|---|---|---|
混合回归 | 固定效应 | 混合回归 | 固定效应 | ||
LnGDP | 0.536*** (9.18) | 0.238*** (3.37) | -0.031 (-0.24) | -0.906*** (-6.27) | |
LnSEC | 0.217** (2.06) | 0.555*** (4.02) | 0.700 (0.38) | 0.656*** (2.58) | |
LnINNO | -0.173*** (-4.30) | -0.249*** (-2.97) | -0.036 (-0.40) | 0.388*** (2.18) | |
LnHPV | 0.0008 (0.03) | -0.008 (-0.31) | -0.218*** (-4.73) | -0.049 (-0.94) | |
LnFDI | -0.233*** (-10.09) | -0.187 (-0.63) | -0.428 (-1.21) | -0.269 (-0.65) | |
LnCOAL | 0.250*** (9.33) | 0.501*** (7.67) | 0.453*** (9.22) | 0.425*** (3.58) | |
常数 | -2.742*** (-4.47) | -4.792*** (-7.04) | -2.748** (-1.87) | 4.926*** (2.75) | |
R2 | 0.594 | 0.430 | 0.440 | 0.503 | |
F统计量 | 61.76*** | 28.62*** | 25.65*** | 28.28*** | |
Log Likelihood | -15.239 | 157.976 | -119.644 | 73.341 | |
Observations | 260 | 260 | 203 | 203 |
表2
固定效应空间滞后模型回归结果"
变量 | 卫星观测核算数据 | 统计年鉴数据 | |||||||
---|---|---|---|---|---|---|---|---|---|
Rook | K=3 | K=4 | K=5 | Rook | K=3 | K=4 | K=5 | ||
LnGDP | 0.144** (2.215) | 0.201*** (3.183) | 0.180*** (2.845) | 0.189*** (2.903) | -0.330*** (-2.920) | -0.435*** (-3.676) | -0.394*** (-3.313) | -0.381*** (-3.267) | |
LnSEC | 0.236* (1.935) | 0.329*** (2.665) | 0.245** (1.973) | 0.241* (1.894) | 0.152 (0.823) | 0.144 (0.733) | 0.110 (0.560) | 0.039 (0.202) | |
LnINNO | -0.191** (-2.574) | -0.207*** (-2.768) | -0.238*** (-3.202) | -0.270*** (-3.532) | 0.302** (2.350) | 0.319** (2.342) | 0.338** (2.481) | 0.319** (2.376) | |
LnHPV | -0.018 (-0.741) | -0.283 (-1.181) | -0.034 (-1.408) | -0.025 (-0.999) | -0.028 (-0.706) | -0.060 (-1.488) | -0.056 (-1.395) | -0.051 (-1.297) | |
LnFDI | -0.041* (-1.663) | -0.050** (-1.980) | -0.043* (-1.727) | -0.390 (-1.511) | -0.044 (-1.478) | -0.036 (-1.119) | -0.047 (-1.472) | -0.046* (-1.470) | |
LnCOAL | 0.415*** (6.912) | 0.434*** (7.114) | 0.447*** (7.413) | 0.458*** (7.405) | 0.224** (2.583) | 0.288*** (3.152) | 0.265*** (2.913) | 0.269*** (2.999) | |
ρ | 0.458*** (7.886) | 0.405*** (7.149) | 0.511*** (7.365) | 0.408*** (5.933) | 0.690*** (13.141) | 0.614*** (11.316) | 0.663*** (12.184) | 0.694*** (12.702) | |
R2 | 0.913 | 0.911 | 0.911 | 0.907 | 0.949 | 0.957 | 0.950 | 0.950 | |
LogLikelihood | 179.857 | 177.660 | 178.768 | 173.655 | 111.507 | 80.701 | 112.120 | 114.696 | |
Observations | 260 | 260 | 260 | 260 | 203 | 203 | 203 | 203 |
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