The IGT mode was improved by introducing an indicator E which represents energy structure, and then the value of E~ was calculated by a fuzzy matrix. Meanwhile, the energy structure of Hunan Province was forecasted by Markov model. The results show that coal, oil, natural gas and hydropower accounts for 54. 75% , 10.75% , 2.75% and 26. 24% respectively in primary energy consumption of Hunan. On the basis of this energy structure, the IGT mode based on energy structure was applied to forecast CO2 emissions of Hunan Province. The results show that CO2 emissions of Hunan Province in 2020 were forecasted to be 48 787.23, 1.51 times as much as that in 2010, however, carbon dioxide emissions per unit of GDP were 1.19 tons/million, 41. 6% lower when compared with that in 2010. Carbon dioxide emissions produced in 2020 reduced 7 049.79 million tons due to optimization of energy structure, accounting for 14. 45% of total CO2 emissions. If the Hunan＇s energy saving rate reaches 7.8% per unit GDP; CO2 emissions will remain the same level, as that of 2010 so as to achieve the targets economic growth cut its ties with CO2 emissions.%将能源结构指标Es引入到IGT模型中，并用模糊矩阵对Es进行计算，同时，采用马尔可夫模型对湖南省能源结构进行预测，结果显示：到2020年，湖南省煤、石油、天然气、水电在一次能源消费中的占比为：54．75％：10．75％：2．75％：26．24％；在此能源结构的基础上，用基于能源结构的IGT模型对湖南省CO2排放量进行预测，预测结果显示：2020年湖南省CO2排放量为48787．23万吨，是2010年的1．51倍，但其单位GDP的CO2排放量仅为1．19吨／万元，比2010年下降41．6％；2020年因能源
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