2001-2009年中国碳排放与碳足迹时空格局

碳排放引发的全球变暖给自然环境及人类社会都带来了显著影响,而碳足迹可以衡量自然生态系统对人类活动碳排放的响应。为研究自然-社会二元系统碳动态,基于MODIS(Moderate Resolution Imaging Spectroradiometer)数据和统计资料计算2001—2009年中国陆地植被净初级生产力、能源消费碳排放、碳足迹和碳赤字;在GIS(Geographic Information System)技术支持下,运用空间自相关分析方法讨论其时空格局;据此划分生态经济区。结果表明:(1)2001—2009年全国植被净初级生产力(Net Primary Production,NPP)平均值为3.32 Pg C/a(1 Pg=1015g),呈西南地区东南沿海华中、华东地区东北、华北地区西北地区的空间格局;(2)2001—2009年全国能源消费碳排放逐年增加,年均增长率16.7%,多年平均值2.53 Pg C/a,呈东部中部西部的空间格局;(3)2001—2009年全国碳足迹逐年增加,年均增长率14.7%,多年平均值6.98×106km2;具有正碳赤字(即碳源)的省份为山西、环渤海地区各省、长三角地区各省、广东;相邻省份碳赤字的相对大小由于互相影响而改变;(4)全国分为中东部、南部、北部、西部四个生态经济大区。研究结果直观揭示了中国碳排放和碳足迹的时空动态,为实现自然-社会二元系统的可持续发展提供科学依据。

Spatiotemporal patterns of carbon emission and carbon footprint in China during 2001-2009

Increasing carbon emission, which has been recognized as one major reason for enhanced global warming, is influenced by complex interactions between natural and anthropogenic processes. The terrestrial ecosystem has been reported as a carbon sink during recent decades; However, this carbon sink has been largely offset by the carbon emission from human activities such as land use change and fossil fuel consumption. An accurate quantification of the carbon fluxes in natural ecosystems in response to human activities is of critical importance for global change study. Using a carbon footprint model, this study characterized the spatiotemporal carbon patterns in the coupled system of nature-society. Specifically, we firstly calculated the net primary productivity (NPP), carbon emission from energy consumption, carbon footprint, and carbon deficit in China during 2001-2009 based on MODIS and statistical data; Then we applied spatial auto-correlation analysis method to quantify spatiotemporal patterns of carbon emission and carbon footprint; Furthermore, we made an eco-economic regionalization of China based on the carbon deficit variation. Four conclusions can be drawn as follows. (1) The mean NPP was 3.32 Pg C/a during 2001-2009 in China (1 Pg = 10¹⁵ g), with small annual variations and obvious spatial heterogeneities, ranking as the southwestern > the southeastern coastal region > the central and the eastern > the northeastern and the northern > the northwestern parts of China. (2) The carbon emission from energy consumption in China increased from 1.52 Pg C/a (2001) to 3.53 Pg C/a (2009) with an annual rate of 16.7% and an average value of 2.53 Pg C/a. Geographically, carbon emission decreased from east to west. (3) Carbon footprint in China increased from 4.46×10⁶ km² (2001) to 9.69×10⁶ km² (2009) with an annual rate of 14.7% and an average value of 6.98×10⁶ km². Positive carbon deficit was found in Shanxi, Bohai Economic Zone, Yangtze River Delta and Guangdong, while negative carbon deficit existed in most provinces of the central and the western China. On the national scale, carbon deficit showed a significantly positive spatial autocorrelation; On the regional scale, however, the eastern China had a high-high clustering, the western China had a low-low clustering, and the northern China had a high-low distribution; Carbon deficit of adjacent provinces influenced each other, suggesting that interprovincial and interregional cooperation could contribute to a positive diffusion effect from the "hotspot" of carbon deficit to the surroundings. (4) China can be divided into four eco-economic zones based on spatiotemporal patterns and local spatial auto-correlation of carbon deficit. The first was the central-eastern region, which was in urgent need of low-carbon development mode considering its rapid economic development and limited availability for ecological resources. The second was the southern region, which had experienced fast economic development in past decades and a provincial cooperation is needed to strengthen the positive ecological effect diffusion in future. The third was the northern region, having unbalanced eco-economic development, where were required resources complementation among different provinces. The fourth was the western region with slow but accelerated economic development, which should stabilize ecological benefits and improve the economic effectiveness. Overall, our integrated modeling framework presented a spatial statistic method for assessing carbon uptake/release patterns and dynamics in the coupled system of nature-society over large areas, which may help integrate biophysical and socioeconomic processes for sustainable development in different regions.