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能源足迹是分析区域经济发展与资源环境关系的重要评价指标和方法。中国能源足迹动态变化及影响因素分析,可为社会经济-资源环境可持续发展提供理论基础。以中国能源消耗统计数据及全国森林资源清查数据为基础,利用能源足迹计算方法和STIRPAT模型,分析了1978~2013年中国能源足迹动态变化和影响因素,结果表明:(1)中国能源足迹呈增加趋势,主要集中在经济和能源消耗量快速增加的2002~2013年。中国能源足迹赤字水平逐步扩大,能源足迹生态压力增大,生态环境安全水平较低,生态风险较高,需引起重视。(2)中国能源足迹构成变动较小,能源足迹构成仍以EEF(煤品)为主,EEF(油品)次之,EEF(天然气)最小,表明中国能源消耗结构调整任重而道远。(3)1978~2013年中国人均GDP和能源足迹产值均呈指数增加趋势,而能源足迹强度呈指数减少趋势。(4)能源足迹与人均GDP、第二产业占GDP比重和单位工业增加值能耗呈正相关关系,与人口数量呈负相关关系。所有影响因素中,人均GDP对能源足迹的解释程度最高,单位工业增加值能耗和第二产业占GDP比重次之,人口数量最低。
The energy footprint is an important indicator and method for analyzing the relationship between regional economic development and resources and environment. The dynamic changes of China’s energy footprint and the analysis of its influencing factors can provide a theoretical basis for socio-economic-resource sustainable development. Based on China’s energy consumption statistics and national inventory data of forest resources, the energy footprint calculation method and STIRPAT model were used to analyze the dynamic changes of China’s energy footprint and its influencing factors from 1978 to 2013. The results show that: (1) China’s energy footprint is increased Trends, mainly in the rapid economic and energy consumption increased from 2002 to 2013. China’s energy footprint deficit gradually expanded, ecological footprint of energy footprint increased, ecological security level is lower, ecological risk is higher, needs attention. (2) China’s energy footprint composition is relatively small. The energy footprint is still dominated by EEF (coal products), followed by EEF (oil products), with the smallest EEF (natural gas), indicating that China’s energy consumption structure adjustment has a long way to go. (3) The output value of China’s per capita GDP and energy footprint increased exponentially from 1978 to 2013, while the energy footprint intensity showed an exponential declining trend. (4) There is a positive correlation between energy footprint and GDP per capita, the proportion of secondary industry to GDP and energy consumption per unit of industrial added value, and a negative correlation with population. Among all the influencing factors, the GDP per capita explained the energy footprint to the highest level, the energy consumption per unit of industrial added value and the secondary industry accounted for the second largest proportion of GDP, and the population was the lowest.