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Climate change is one of the world’Stop ten environmental issues. Many studies show that the climate change is caused by human activities. The driving factor of global warming is greenhouse gases, particularly the carbon dioxide whose warming effect is up to 63%. China proposes "develop low carbon economy" in recent years, jiangsu puts forward the slogan that building" low carbon province". Consequently, grasp themacro characteristics of a region’Scarbon emissions, and then take measures to reduce the number of carbon emissions with apurpose are significance of this magnificent.This article reviews the current domestic and international research on measure carbon emissions, carbon emission pattern, and the relationship between economic growth and carbon emissions, Accounts energy consumption and carbon emissions from agricultural and forest grassland carbon sequestration form 1996 to 2008 in Jiangsu Province and the 13 cities. Correlation coefficient, coefficient of variation, weight transfer analysis, the Gini coefficient were applied to analyzing the Temporal-spatil Variability of Carbon Emissions in Jiangsu Province.based on curve fitting and decoupling analysis, this article analyzed how the economic growth influence carbon emissions, and put forward proposals on reducing carbon emissions.Taking city as a spatial unit and per capita carbon emissions, carbon emission intensity and decoupling index as measuring indexes for the level of regional carbon emissions from 1996 to 2008, it was found that, first, carbon emissions were found to increase annually by 13% over the entire Jiangsu Province during the latest 13 years, with emitting carbon of 196.4 million tons in 2008; Jiangsu Province has a more equitable distribution pattern, but the center of gravity of carbon emissions continues to shifting to the Southern should pay attention to.Per capita carbon emissions in well-developed cities are larger with relatively lower carbon intensity; economically backward cities have a smaller amount of per capita carbon emissions, but carbon emission intensity increased rapidly. Second, the differences of per capita carbon emissions and carbon emission intensity among 13 cities are gradually narrowed. Third, per capita carbon emissions increased linearly with the GDP per capita in both Jiangsu Province and the municipalities, and Nanjing, Suzhou, Yangzhou, Xuzhou, as the more devepoled cities in differrent regions have a more rapidly growth rate. Fourth, there existed a reversed U shaped curve between carbon emission intensity and GDP per capita, with the inflexion point of the curve at roughly 25,000 yuan per capita, while in each city, the relationship is a wavy curve. Fifth, decoupling index is influenced by the industrial structure, economic policy and the level of clean technologies.
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