中国10km二氧化碳排放网格及空间特征分析  

China 10km carbon dioxide emissions grid dataset and spatial characteristic analysis

作　　者：王金南[1] 蔡博峰[1] 曹东[1] 刘兰翠[1] 周颖[1] 张战胜[1] 薛文博[1] 

机构地区：[1]环境保护部环境规划院,气候变化与环境政策研究中心,北京100012

出　　处：《中国环境科学》2014年第1期1-6,共6页China Environmental Science

基　　金：环境保护部温室气体监管能力建设和典型行业减排对策(1441100014)

摘　　要：基于全国第一次污染源普查数据中150多万家企业数据等,'自下而上'建立中国2007年10km×10km CO2排放网格数据.结果显示,中国CO2排放空间格局的特点是基本沿着我国人口胡焕庸线分为东部和西部,东部地区明显高于西部地区.全国CO2排放明显受城市活动影响,网格排放高值区域都是以北京、上海、广州等大型城市为核心的区域.京津冀、长江三角洲、珠江三角洲地区是我国CO2排放空间格局的重点地区.全局Moran指数表明,中国CO2排放空间格局在10km空间分辨率水平上具有显著的正空间自相关性,即空间上存在显著的集聚效应,而非随机杂散分布.局部Moran指数显示中国CO2排放在空间上具有显著集聚效应的区域面积并不大,主要集中在北京、上海、广州等重点城市核心区周边.基于这些重点城市采取CO2减排政策和措施,由于带动效应,其实际减排效果要远大于直接减排效果.Based on the exclusively statistics on 1.58 million industrial enterprises in the First China Pollution Source Census, 10km resolution CO2 emissions grid of China was built bottom-up by point emission sources. Our results showed the spatial pattern of CO2 emissions of China was distinctly marked by the China＇s population HuHuanYong line. The CO2 emissions in the eastern region of this line were obviously higher than that in the western region. Hotspot cities, suchas Beijing, Shanghai, Guangzhou had decisive effluence on the spatial pattern of emissions of China. The Jing-Jin-Ji region, Yangtze River Delta region and Pearl River Delta region were the key regions of CO2 emissions. The calculated global Moran＇s I indicated that there was significant spatial autocorrelation in CO2 emissions of China. The local Moran＇s I showed that the spatial autocorrelation concentrated in some hotspot regions centered by hotspot cities, which implied that the CO2 emissions reduction strategies would receive multiplied effects when implemented on these cities.

关 键 词：CO2排放 10km网格 空间特征 

分 类 号：X32[环境科学与工程—环境工程]