Acta Geographica Sinica ›› 2013, Vol. 68 ›› Issue (11): 1513-1526.doi: 10.11821/dlxb201311007

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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

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