基于夜间灯光数据的中国能源消费碳排放特征及机理

doi:10.11821/dlxb201311007

Abstract

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

SU Yongxian, CHEN Xiuzhi, YE Yuyao, WU Qitao, ZHANG Hong'ou, HUANG Ningsheng, KUANG Yaoqiu. The characteristics and mechanisms of carbon emissions from energy consumption in China using DMSP/OLS night light imageries[J].Acta Geographica Sinica, 2013, 68(11): 1513-1526.

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

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