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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
引用本文: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.
作者姓名:姜磊  何世雄  崔远政
作者单位:浙江财经大学经济学院,浙江 杭州 310018
浙江财经大学土地与城乡发展研究院,浙江 杭州 310018
基金项目: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)
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: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。中国省域氮氧化物排放存在显著的空间正自相关现象。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  
收稿时间:2019-01-24
修稿时间:2019-04-11

Analysis of Driving Factors of Nitrogen Oxides Emissions in China Based on Spatial Econometric Models: Data from Satellite Observations
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.
Authors:Jiang Lei  He Shixiong  Cui Yuanzheng
Institution:School of Economics, Zhejiang University of Finance and Economics, Hangzhou 310018, Zhejiang, China
Institute of Land and Urban-rural Development, Zhejiang University of Finance and Economics, Hangzhou 310018, Zhejiang, China
Abstract:Nitrogen Oxides (NOx) 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 NOx emissions. The existing literature on NOx 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 NOx 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 NOx 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 NOx emissions. The findings are the following. 1) High NOx 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 NOx 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 NOx emissions in China. On the other hand, technological progress and foreign direct investment have negative effects on NOx emissions, indicating that advanced technologies may be a best solution to reducing NOx emissions and improving the environmental quality. Finally, policy implications based on the main findings of the research were discussed.
Keywords:Nitrogen Oxides emissions  spatial econometric model  satellite observations  
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