基于夜间灯光数据的黄河流域能源消费碳排放时空演变多尺度分析

Multiscale Spatio-Temporal Characteristics of Carbon Emission of Energy Consumption in Yellow River Basin Based on the Nighttime Light Datasets

采用四步法对DMSP-OLS和NPP-VIIRS夜间灯光数据进行融合校正,得到1995—2016年黄河流域长时间序列夜间灯光数据集,结合省级能源消费统计碳排放数据,构建黄河流域多尺度碳排放估算模型。利用构建的估算模型和探索性空间数据分析方法从省级、市级、县级和栅格级对黄河流域能源消费碳排放进行时空演变特征分析。结果表明:①融合校正模型拟合优度为0.8354,满足精度要求;多尺度碳排放估算模型拟合优度达到0.8482,精度检验平均相对误差为17.25%,估算模型有效。②碳排放量呈现出显著连片扩张趋势;碳排放量较高省份为内蒙古、山西和河南;较高城市主要分布在山东、陕西和内蒙古。高碳县域基本维持在四川广元和内江,山西太原的清徐县,内蒙古的乌审旗和准格尔旗,山东枣庄、济宁和济南。③碳排放具有显著的空间正相关性;省级尺度全局相关性增长幅度最大;省级碳排放仅有甘肃呈现出LL集聚格局;城市聚集态势由内蒙古和山东的高碳聚集区以及甘肃和青海的低碳聚集区引起;县域聚集态势由高碳聚集区和低碳聚集区引起。

This study applies the four-step method to integrate and correct the DMSP-OLS and NPP-VIIRS nighttime light datasets,so as to obtain the long time series of nighttime light datasets in the Yellow River Basin from 1995 to 2016.and constructs the multiscale carbon emissions estimation model for the Yellow River Basin based on the statistical data of provincial energy consumption.This paper analyzes the spatio-temporal characteristics of carbon emissions of energy consumption in the Yellow River Basin from the provincial,prefectural,county and grid scales by using the estimation model and the exploratory spatial analysis.The results show that:1)The fitting precision using integrated model is 0.8354,which meets the accuracy requirement.And the fitting precision reaches 0.8482 based on multiscale carbon emissions estimation model,the average relative error of accuracy test is 17.25%,which indicates the estimation model is effective.2)It shows a significant continuous expansion on the carbon emissions.Inner Mongolia Autonomous Region,Shanxi Province and Henan Province are at the highest level on carbon emissions.The cities which are located in Shandong Province,Shaanxi Province and Inner Mongolia Autonomous Region are mainly at the higher level of carbon emissions.The counties with higher level of carbon emissions are basically distributed in Guangyuan and Neijiang in Sichuan Province,Qingxu in Shanxi Province,Uxin Banner and Jungar Banner in Inner Mongolia Autonomous Region,and Zaozhuang,Jining and Jinan in Shandong Province.3)It has significant positive spatial correlation on the carbon emissions,and the growth of the global correlation on provincial scale is the largest.Only Gansu Province shows low-low agglomeration pattern.The urban agglomeration trend is caused by the high carbon accumulation areas in Inner Mongolia Autonomous Region and Shandong Province,as well as by the low carbon accumulation areas in Gansu Province and Qinghai Province.The agglomeration trend at the county level is caused by high and low carbon accumulation areas.