中国工业氮氧化物排放的时空分布特征及驱动因素分析

doi:10.18402/resci.2016.09.14

摘要/Abstract

摘要：

作为“十二五”期间新纳入控制性约束指标的污染物,NO_X排放量的研究成果可为地区减排份额的制定提供依据。本文运用ESDA（探索性空间方法）分析省域工业NO_X排放的时空分布特征,进而通过LMDI模型分解探究其主要驱动因素。结果显示：①工业NO_X的排放总量虽然呈现先增加后减少的态势,但至2013年也只完成减排份额的5.6%,相对2015年15%的减排目标还有一定距离,减排形势严峻;②省域工业NOx排放在空间上呈现集聚分布特征,高排放量集聚主要出现在河北、河南、山东、江苏等中东部地区,且随时间的推移有向外围省份扩张的趋势;③LMDI模型分解结果表明,经济发展是NOx增排的主要驱动力,生产技术进步和能源利用效率提升是减排的主要控制因素,产业结构调整的减排效应在2011年后开始凸显;④以四象限图及排放量为划分依据将各省份划分为3个大类,并从改进生产技术、提高能源利用效率、增加经济鼓励、削减排放份额等方面提出相应的减排建议。

关键词: 工业氮氧化物, 时空特征, ESDA, LMDI模型, 驱动因素, 污染减排

Abstract:

As included in the Twelfth Five-year Plan constraint control index,achievements in NOx emissions research are critical to formulating proper policies to reduce China’s air pollution. Based on provincial data for 2006 to 2013,we used ESDA to analysis temporal-spatial distribution characteristics of provincial industrial NOx emissions and then through LMDI model decomposition the main driving factors. We found that industrial NOx emissions first increase then decrease,2011 is the turning point. Only 5.6% of emissions reduction was completed by 2013 and the current situation remains severe emissions reduction. NOx emissions have a concentration distribution in space：high emissions of provincial agglomeration appeared mainly in Hebei,Shandong and Jiangsu,represented by the area of central China and with the passage of time has trended towards southern and western China. LMDI decomposition model results show that economic development is the main driving force of the increase in NOx emissions,meaning that NOx emissions in many provinces are increased with economic growth. At the same time,production technology progress and energy utilization efficiency is the main driving force of emission reduction,industrial structural adjustment of abatement effect began to highlight after 2011. The provinces can be divided into three classes,and depending on the characteristics of these three types of provinces we can put forward different corresponding suggestions to reduce emissions,such as improving production technology,the efficiency of energy utilization,economic encouragement and cutting the emissions share.

Key words: NOx, temporal and spatial distribution characteristics, ESDA, LMDI model, driving factor, pollution reduction

刁贝娣, 曾克峰, 苏攀达, 丁镭, 刘超. 中国工业氮氧化物排放的时空分布特征及驱动因素分析[J]. 资源科学, 2016, 38(9): 1768-1779.

DIAO Beidi,ZENG Kefeng,SU Panda,DING Lei,LIU Chao. Temporal-spatial distribution characteristics of provincial industrial NOx emissions and driving factors in China from 2006 to 2013[J]. Resources Science, 2016, 38(9): 1768-1779.

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年份	Moran’s I	E（I）	Sd.	P-value
2006	0.227 8	-0.303	0.115 2	0.021
2007	0.058 6	-0.303	0.115 5	0.203
2008	0.192 1	-0.303	0.111 5	0.034
2009	0.173 2	-0.303	0.114 2	0.038
2010	0.161 7	-0.303	0.108 9	0.045
2011	0.221 8	-0.303	0.113 4	0.023
2012	0.227 8	-0.303	0.114 9	0.023
2013	0.213 5	-0.303	0.112 8	0.024

	高排放等级省域	中低排放等级省域
最佳省域	广东、江苏、辽宁、浙江、安徽	湖北、上海、北京
积极省域	山西、福建、陕西（一类）	四川、湖南、重庆、甘肃、江西、天津
问题省域	山东、河南、河北（二类）	黑龙江、吉林
落后省域	新疆、内蒙古（三类）	青海、广西、云南、贵州、宁夏、海南