基于卫星夜间灯光数据的中国分省碳排放时空模拟

发布时间：2018-07-07 21:41

本文选题：DMSP/OLS夜间灯光数据 + 碳排放　；参考：《中国人口·资源与环境》2017年09期

【摘要】：中国能源统计数据"横向不可比,纵向不可加"现象依然突出,尤其是分省能源消费统计千差万别,给分省碳排放评估带来了较大困难,如何利用卫星遥感数据科学合理地估算中国分省碳排放是当前亟须研究的问题。本文运用DMSP/OLS全球稳定夜间灯光数据,在通过相互校正、年内融合和年际间校正等系列处理得到中国分省稳定夜间灯光数据的基础上,首先分别构建中国分省稳定夜间灯光亮度DN值与人均碳排放和单位面积碳排放之间的时空地理加权回归模型,两个模型整体效果均较好,拟合优度分别高达96.74%和99.24%;其次运用稳定夜间灯光亮度DN值对分省人均碳排放和单位面积碳排放进行时空模拟;最后运用人口规模和土地面积对分省碳排放进行估算。估算结果显示:(1)整体来看,2000—2013年年均碳排放模拟值与实际值6.3349×109t较为接近,两个模型的相对误差均在0.5%以内。(2)分年度来看,所有年份的相对误差均在5%以内,2006年分省加总碳排放模拟值与实际碳排放6.2036×109t最为接近,绝对误差和相对误差均较小,两个模型模拟值的相对误差均为0.04%。(3)分省域来看,2000—2013年年均碳排放模拟值与实际碳排放均非常接近,除海南和宁夏外,其余28个省区市的相对误差均在1%以内。(4)分年度分省域来看,以2013年为例,40%省份的相对误差在2%以内,70%省份的相对误差在5%以内。从整体、分年度、分省域、分年度分省域的估算结果来看,基于稳定夜间灯光数据的中国分省碳排放时空模拟效果良好。因此,运用卫星夜间灯光数据可以较为准确地对中国分省碳排放进行估算和预测,为卫星遥感影像数据服务分省碳排放监测和评估提供一种补充性参考。
[Abstract]:The phenomenon of "horizontal incomparable, vertical insuperable" in China's energy statistics is still prominent, especially the disparity of energy consumption statistics by province, which brings great difficulties to the assessment of carbon emissions by province. How to use satellite remote sensing data scientifically and reasonably to estimate China's carbon emissions by province is an urgent problem to be studied at present. In this paper, the global stable night light data of DMSPP / OLS are used to obtain stable night light data from provinces of China through a series of processing such as mutual correction, intra-year fusion and inter-annual correction. Firstly, the spatio-temporal and geographical weighted regression models between the stable luminance DN value and the carbon emission per capita and carbon emission per unit area of each province in China are constructed, respectively. The two models have good overall effect. The goodness of fit is as high as 96.74% and 99.24% respectively. Secondly, the space-time simulation of carbon emissions per capita and carbon emissions per unit area is carried out by using the DN value of light brightness at night. Finally, the estimation of carbon emissions per province is carried out by using population scale and land area. The results show that: (1) the simulation value of carbon emissions from 2000 to 2013 is close to the actual value of 6.3349 脳 10 ~ (9) t, and the relative error of the two models is within 0.5%. (2) from the point of view of each year, The relative error of all years is less than 5%, and the simulation value of total carbon emission in 2006 is the closest to the actual carbon emission of 6.2036 脳 10 9 t, and the absolute error and relative error are small. The relative error of the simulated values of the two models is 0.04. (3) the simulated average annual carbon emissions from 2000 to 2013 are very close to the actual carbon emissions, except for Hainan and Ningxia. The relative error of the other 28 provinces is within 1%. (4) the relative error of 40% provinces is within 2% or 70% of the provinces in 2013, and the relative error of the provinces is less than 5%. From the estimation results of the whole, annual, provincial and annual, it is found that the spatio-temporal simulation of carbon emissions in China based on stable night lighting data is effective. Therefore, satellite night light data can be used to estimate and predict the carbon emissions of each province in China, which provides a supplementary reference for the monitoring and evaluation of carbon emissions by province in satellite remote sensing image data service.
【作者单位】：国家信息中心;
【基金】：国家重点研发计划项目“气候变化经济影响综合评估模式研究”(批准号:2016YFA0602601);国家重点研发计划项目“中国实现2030年碳排放峰值目标的优化路径研究”(批准号:2016YFA0602800) 国家科技重大专项“涪陵页岩气开发示范工程”(批准号:2016ZX05060) 国家自然科学基金项目“支撑省级能源规划评估的能源需求预测模型体系研究”(批准号:71573062)
【分类号】：X321;X87

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