地理学报 ›› 2013, Vol. 68 ›› Issue (11): 1513-1526.doi: 10.11821/dlxb201311007

• 环境研究 • 上一篇    下一篇

基于夜间灯光数据的中国能源消费碳排放特征及机理

苏泳娴1,3,4, 陈修治2, 叶玉瑶1, 吴旗韬1, 张虹鸥1, 黄宁生3, 匡耀求3   

  1. 1. 广州地理研究所, 广州510070;
    2. 中国科学院华南植物园, 广州510650;
    3. 中国科学院广州地球化学研究所, 广州510640;
    4. 中国科学院大学, 北京100049
  • 收稿日期:2013-04-05 修回日期:2013-10-11 出版日期:2013-11-20 发布日期:2013-11-20
  • 通讯作者: 张虹鸥(1962-),男,研究员,中国地理学会会员(S110000213M),近年来主要从事城市与区域规划等研究。E-mail:hozhang@gdas.ac.cn
  • 作者简介:苏泳娴(1985-),女,硕士,助理研究员,主要从事区域生态与区域发展研究。E-mail:suyongxian@163.com
  • 基金资助:
    “十二五”国家科技支撑计划项目(2012BAJ15B02);国家自然科学基金项目(41001385);广东省科技计划项目(2011B031100003)

The characteristics and mechanisms of carbon emissions from energy consumption in China using DMSP/OLS night light imageries

SU Yongxian1,3,4, CHEN Xiuzhi2, YE Yuyao1, WU Qitao1, ZHANG Hong'ou1, HUANG Ningsheng3, KUANG Yaoqiu3   

  1. 1. Guangzhou Institute of Geography, Guangzhou 510070, China;
    2. South China Botanical Garden, CAS, Guangzhou 510650, China;
    3. Guangzhou Institute of Geochemistry, CAS, Guangzhou 510640, China;
    3. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2013-04-05 Revised:2013-10-11 Online:2013-11-20 Published:2013-11-20
  • Supported by:
    The National Science & Technology Pillar Program during the 12th Five-year Plan Period, No.2012BAJ15B02; National Natural Science Foundation of China, No.41001385; Science & Technology Plan Project Grant, Guangdong province, China, No.2011B031100003

摘要: 本研究基于DMSP/OLS夜间灯光影像实现了1992-2010 年以市级为基础单元的我国碳排放估算,弥补了统计数据不全、统计口径不一的缺点。从全国、4 个经济区和6 大城市群3 个层面的碳排放分析结果显示,我国CO2排放总量持续增长,各地区、省市增速各不相同,空间聚集程度越来越明显,基本形成了“东部沿海城市高高集聚,西部欠发达城市低低集聚”的格局。人均碳排放强度基本呈现为“东部> 东北部> 西部> 中部”,单位GDP碳排放强度则呈现为“东北部和西部较高”、“东部和中部较低”。GDP增长是决定CO2排放总量增长的主导因素,而能源结构、能源利用效率、产业结构是影响碳排放强度的主要原因。对于西部和东北部等以能源和重工业为主导产业的城市,其减排策略应着重能源结构优化和能源利用效率的提高。对于东部和中部等以技术、劳动密集型和轻工业为主导产业的城市,其减排策略应侧重于产业结构调整和转型升级。

关键词: 时空变化, DMSP/OLS夜间灯光影像, 遥感, 机理, 碳排放

Abstract: It is critical for China to make the emission reduction targets and development of the scientific emission reduction planning in the future. On the basis of the DMSP/OLS night light imageries, this research estimates the China's city-level carbon emissions from 1992 to 2010. This makes up the vacancies of statistical carbon emission data and overcomes the inconsistence of statistical carbon emission methods. Analysis results from three scales (the whole mainland of China, 4 economic regions and 6 urban agglomerations) show that the national CO2 emissions grew continually, but varied from place to place. What is more, the spatial agglomeration of China's CO2 emissions has become more and more obvious, which have led to the current CO2 emission pattern—"high-high concentration in eastern coastal cities and low-low concentration in western undeveloped cities". The carbon emission intensity of per capita basically maintains as the "Eastern > Northeastern > Western > Central" pattern. The carbon emission intensity of per GDP shows the "Higher in Northeastern and Western China" and "Lower in Eastern and Central China" pattern. Growth rate of GDP is the major factor affecting the increasing speed of carbon emissions. Energy structures, energy efficiencies, industrial structures are the three main factors influencing the carbon intensities. As for the Western and Northeastern regions, whose industries are mainly energy-related and heavy ones, their best mitigation policies should be optimizing the energy structure and increasing energy use efficiently.

Key words: mechanism, remote sensing, DMSP/OLS night light imagery, temporal and spatial variation, carbon emission