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

doi:10.13249/j.cnki.sgs.2020.03.004

地理科学 ›› 2020, Vol. 40 ›› Issue (3): 364-373.doi: 10.13249/j.cnki.sgs.2020.03.004

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

姜磊¹, 何世雄¹, 崔远政²()

^1. 浙江财经大学经济学院,浙江杭州 310018
^2. 浙江财经大学土地与城乡发展研究院,浙江杭州 310018

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:
浙江省哲学社会科学规划课题(20NDQN303YB);教育部人文社会科学青年基金项目(17YJC790061);广东省自然科学基金团队项目(2018B030312004)(2018B030312004);浙江省自然科学基金项目(LY19G030013);浙江省自然科学基金项目(LQ19D050001);国家自然科学基金项目资助(41901170)

Analysis of Driving Factors of Nitrogen Oxides Emissions in China Based on Spatial Econometric Models: Data from Satellite Observations

Jiang Lei¹, He Shixiong¹, Cui Yuanzheng²()

^1. School of Economics, Zhejiang University of Finance and Economics, Hangzhou 310018, Zhejiang, China
^2. Institute of Land and Urban-rural Development, Zhejiang University of Finance and Economics, Hangzhou 310018, Zhejiang, China

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:
Project of Philosophy and Social Science of Zhejiang Province(20NDQN303YB);Humanities and Social Science Research Program of the Ministry of Education(17YJC790061);National Natural Science Foundation of Guangdong Province(2018B030312004);National Natural Science Foundation of Zhejiang Province(LY19G030013);National Natural Science Foundation of Zhejiang Province(LQ19D050001);National Natural Science Foundation of China(41901170)

摘要/Abstract

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。

关键词: 氮氧化物排放, 空间计量模型, 卫星观测

Abstract:

Nitrogen Oxides (NO_x) is one of major contributors to degrading the environmental quality in China. Moreover, it notably leads to haze and fog events. Hence, it is of great significance to study what determines NO_x emissions. The existing literature on NO_x emissions in China has been conducted mainly based on bottom-up method with the data from statistical yearbooks, which might suffer from statistical bias. Unlike these empirical studies, we obtained a data set of NO_x emissions of 26 Chinese provinces from 2007 to 2016 estimated from satellite observations. In the first stage, this research analyzed the spatio-temporal characteristics of NO_x emissions at provincial levels over China. Then, in the second stage it applied spatial econometric panel data models to discover the socio-economic driving factors of NO_x emissions. The findings are the following. 1) High NO_x emissions are basically concentrated on the industry-dominated or heavy-industry-dominated provinces, for example, Hebei province, Shanxi Province, Shandong Province, Henan Province, Inner Mongolia autonomous region, and on economically developed eastern provinces, such as Jiangsu Province and Guangdong province. 2) The results of Moran tests illustrated that provincial NO_x emissions exhibited a significant spatial autocorrelation in space, implying spatial clusters. 3) From the results of spatial lag panel data models, the spatial autoregressive coefficient is significant and positive, indicating positive spatial spillovers. Besides, we observed that increases in provincial GDP, the share of the secondary industry, and coal consumption led to the rise of NO_x emissions in China. On the other hand, technological progress and foreign direct investment have negative effects on NO_x emissions, indicating that advanced technologies may be a best solution to reducing NO_x emissions and improving the environmental quality. Finally, policy implications based on the main findings of the research were discussed.

Key words: Nitrogen Oxides emissions, spatial econometric model, satellite observations

中图分类号:

F205

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.

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变量	卫星观测核算数据		统计年鉴数据
变量	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

变量	卫星观测核算数据				统计年鉴数据
变量	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

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

Analysis of Driving Factors of Nitrogen Oxides Emissions in China Based on Spatial Econometric Models: Data from Satellite Observations

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