柏玲,罗溢斌,姜磊,杨东阳,崔远政.中国城市NOx排放的时空特征与驱动因素:基于空间分异视角[J].环境科学学报,2020,40(2):687-696
中国城市NOx排放的时空特征与驱动因素:基于空间分异视角
- Spatio-temporal characteristics and influencing factors of China's urban NOx emissions: A spatial stratified heterogeneity perspective
- 基金项目：浙江省哲学社会科学规划课题（No.20NDQN303YB）；国家自然科学基金（No.41761021）；浙江省自然科学基金项目（No.LY19G030013，LQ19D050001）；浙江省统计研究课题（No.19TJQN05）
- 姜磊
- 1. 南昌大学经济管理学院, 南昌 330031;2. 浙江财经大学经济学院, 杭州 310018
- 杨东阳
- 河南大学黄河文明与可持续发展研究中心, 开封 475001
- 崔远政
- 浙江财经大学土地与城乡发展研究院, 杭州 310018
- 摘要：空气质量的改善是当前中国社会经济转型及实现绿色可持续发展的重要目标之一.基于中国268个城市2007-2016年的氮氧化物（Nitrogen Oxide Emissions，NOx）排放量数据，首先利用自然正交函数（Empirical Orthogonal Function，EOF）分析了268个城市NOx排放的时空演变特征，然后采用一种新的空间分异性分析方法"地理探测器"从空间异质性视角探讨了NOx排放的社会经济驱动因素.结果表明：①EOF第一模态特征向量的高值出现在京津冀地区、山东半岛的淄博、潍坊、济宁和临沂，以及长三角的上海、无锡、南京、苏州和杭州；低值则集中在西南的云贵地区、东南的广东、福建及西北的宁夏.②年尺度上NOx排放的时间系数变化大致呈现先降后升再降的非线性波动.③因子探测分析结果显示，民用汽车总量对NOx排放分布的影响最大，其次是城市人口和工业总产值.不同风险因子的交互作用均大于单因子的作用，其中，城市人口与人均GDP因子之间的交互作用强度最大，工业总产值与民用汽车总量的交互作用强度次之，人均GDP与城市建设用地面积的交互作用强度排第3.④风险区探测结果显示，社会经济驱动因子中的城市人口、人均GDP、工业总产值、城市建设用地面积、全社会用电量和民用汽车总量均与NOx排放呈正相关.京津冀、山东半岛和长三角等发达城市为NOx排放的高风险区，是社会经济驱动因素的多个风险因子共同作用的结果.
- Abstract：Air quality improvements are of great significance to achieve the goals of China's socio-economic transformation and green sustainable development. The main objective of this research is to uncover the spatio-temporal characteristics of Nitrogen Oxide Emissions (NOx) of 268 Chinese cities for the period of 2007-2016, which are estimated from satellite observations and Chemical Transport Models, and then investigate the socio-economic influencing factors of NOx emissions of Chinese cities from a novel perspective of spatial stratified heterogeneity, based on a geographical detector method. The findings are as follows. ① The results of the empirical orthogonal function (EOF) decomposition analysis showed that high values of the first mode eigenvector were basically concentrated on the Beijing-Tianjing-Hebei Region, and 4 cities of the Shandong Peninsula, namely Zibo, Weifang, Ji'ning and Linyi, and 5 cities of the Yangtze River Delta, namely Shanghai, Wuxi, Nanjing, Suzhou and Hangzhou. On the other hand, low values were mainly found in Southwestern Yunnan Province and Guizhou Province, Southeastern Guangdong Province and Fujian Province and Northwestern Ningxia Autonomous Region. ② The time coefficients of EOF from the yearly dimension presented N-shaped fluctuations. ③ The factor detector analysis results displayed that the foremost contributor to NOx emissions in China was vehicle stock, followed by urban population, and industrial development. In addition, the interaction of two factors played a more important role in affecting NOx emissions than each factor separately. Furthermore, the interaction of urban population factor and per capita GDP factor had the biggest impact on NOx emissions, followed by the interaction of total industrial output and vehicle stock, and the interaction of per capita GDP and urban built-up area. ④ The risk area detector analysis results revealed that 6 socio-economic influencing factors, namely, urban population, per capita GDP, total industrial output, urban built-up area, electricity consumption and vehicle stock drove up NOx emissions. Lastly, the Beijing-Tianjing-Hebei Region, the Shandong Peninsula, and the Yangtze River Delta were high NOx emissions risk regions in China, which were mainly caused by interactions of multiple socio-economic factors.
