李峰, 刘军, 刘文龙, 廖顺宝. 京津冀县域夜间灯光数据碳排放时空动态分析[J]. 信阳师范学院学报(自然科学版), 2021, 34(2): 230-236. DOI: 10.3969/j.issn.1003-0972.2021.02.010
引用本文: 李峰, 刘军, 刘文龙, 廖顺宝. 京津冀县域夜间灯光数据碳排放时空动态分析[J]. 信阳师范学院学报(自然科学版), 2021, 34(2): 230-236. DOI: 10.3969/j.issn.1003-0972.2021.02.010
LI Feng, LIU Jun, LIU Wenlong, LIAO Shunbao. Spatiotemporal Dynamics Analysis of Carbon Emissions From Nighttime Light Data in Beijing-Tianjin-Hebei Counties[J]. Journal of Xinyang Normal University (Natural Science Edition), 2021, 34(2): 230-236. DOI: 10.3969/j.issn.1003-0972.2021.02.010
Citation: LI Feng, LIU Jun, LIU Wenlong, LIAO Shunbao. Spatiotemporal Dynamics Analysis of Carbon Emissions From Nighttime Light Data in Beijing-Tianjin-Hebei Counties[J]. Journal of Xinyang Normal University (Natural Science Edition), 2021, 34(2): 230-236. DOI: 10.3969/j.issn.1003-0972.2021.02.010

京津冀县域夜间灯光数据碳排放时空动态分析

Spatiotemporal Dynamics Analysis of Carbon Emissions From Nighttime Light Data in Beijing-Tianjin-Hebei Counties

  • 摘要: 为了更准确地分析京津冀县域碳排放时空分布状况,通过温度EVI调节夜间灯光城市指数(LERNCI)和灯光阈值分别校正DMSP-OLS和NPP-VIIRS夜间灯光数据,结合中国30个省份的碳排放统计数据,构建2000-2016年1 km像元级的碳排放模型,分析京津冀碳排放时空格局变化特征及其影响因素.结果表明:(1)一元二次多项式碳排放回归模型碳排放模拟的平均相对误差为0.37%,可用于碳排放的分析.(2)京津冀碳排放呈现先升后降的趋势,由于节能减排措施的实施,碳排放在2013年出现拐点;碳排放的重点区域位于“京津唐”地区.(3)人口规模和第二产业比重与碳排放呈正相关关系,但是二者的影响力在减弱;人均GDP对碳排放转向负相关关系;城镇化率、固定资产投资和社会劳动生产率对碳排放具有正向推动力.

     

    Abstract: In order to more accurately analyze the spatiotemporal distribution of carbon emissions in Beijing-Tianjin-Hebei counties, DMSP-OLS and NPP-VIIRS nighttime light (NTL) data were corrected by LST and EVI Regulated NTL City Index (LERNCI) and light threshold, respectively, and pixel-level carbon emission models with 1 km resolution were constructed from 2000 to 2016 by combining carbon emission statistics from 30 provinces in China. The spatiotemporal patterns of carbon emissions and influencing factors in Beijing-Tianjin-Hebei region were analyzed. The results show that:(1) the average relative error of carbon emission simulation in the regression model of unary quadratic polynomial carbon emission is 0.37%, which can be used for carbon emission analysis. (2) Carbon emissions in Beijing-Tianjin-Hebei region shows a trend of first rising and then falling. Due to the implementation of energy conservation and emission reduction measures, carbon emissions appears an inflection point in 2013. The key areas of carbon emissions are located in the “Beijing-Tianjin-Tang” region. (3) Population size and proportion of secondary industry are positively correlated with carbon emission, but their influence is weakening. Per capita GDP is negatively correlated with carbon emissions; Urbanization rate, fixed asset investment and social labor productivity have positive impetus to carbon emission.

     

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