【摘要】 采用空间自相关(全局空间自相关和局域空间自相关)分析方法,对1995-2011年广东省能源消费碳排放总量、人均能源消费碳排放量和能源消费碳排放强度3个碳排放指标的空间自相关性和差异性进行研究。结果显示,1995-2011年,能源消费碳排放总量和人均能源消费碳排放量均存在显著全局空间自相关性,2个指标的全局Moran′s I指数分别在0.2~0.3和0.4~0.6范围变动,且均呈先上升后缓慢下降变化趋势,均在2008年达到阶段性极值点。局域空间自相关分析结果表明,这2个碳排放指标在珠三角都有高-高聚集区,在粤北都有低-低聚集区,同时这2个碳排放指标在珠三角周边城市也存在显著局域空间异质区。能源消费碳排放强度不存在显著全局空间自相关性,但在2011年存在一个局域低-低显著自相关区域。研究结果为广东省制定差异化碳减排政策和实现低碳省建设提供决策依据和信息支持。更多还原

【Abstract】 Research on total energy-related carbon emission and energy-related carbon emissions per capita and energy-related carbon emission intensity in Guangdong Province from the year 2005 to 2011 by spatial autocorrelation analysis, include global spatial autocorrelation analysis and local spatial autocorrelation analysis. The results show that total energy-related car-bon emission and energy-related carbon emissions per capita exist significant global spatial autocorrelation from the year 2005 to 2011. Global Moran′s I of total energy-related carbon emission and energy-related carbon emissions per capita change in the range of 0.2 to 0.3 and 0.4 to 0.5 respectively. And both of them increase first and then decline slowly since their peak level in 2008. Results of local spatial autocorrelation analysis show that total energy-related carbon emission and energy-related carbon emissions per capita both have high-high agglomeration in the Pearl River Delta and low-low agglomeration in north region of Guangdong Province. At the same time, this two carbon emission index also exist spatial heterogeneity around the Pearl River Delta. Energy-related carbon emission intensity not exist global spatial autocorrelation, but exist a low-low significant spatial autocorrelation local region in 2011. The results and proposals provide decision basis and information support for Guangdong to make discrepant carbon emission reduction policy and implement low carbon province construction.更多还原