结合“珞珈一号”夜间灯光与城市功能分区的广州市碳排放空间分布模拟及其影响因素分析

doi:10.12082/dqxxkx.2022.210610

地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (6): 1176-1188.doi: 10.12082/dqxxkx.2022.210610

• 地理空间分析综合应用 • 上一篇下一篇

结合“珞珈一号”夜间灯光与城市功能分区的广州市碳排放空间分布模拟及其影响因素分析

卢奕帆(), 梁颖然, 卢思言, 肖钺, 何小钰, 林锦耀()

广州大学地理科学与遥感学院,广州 510006

收稿日期:2021-11-15 修回日期:2021-12-01 出版日期:2022-06-25 发布日期:2022-08-25
通讯作者: *林锦耀(1989— ),男,广东广州人,博士,副教授,主要从事地理建模与遥感应用研究。E-mail: ljy2012@gzhu.edu.cn
作者简介:卢奕帆(1998— ),女,广东潮州人,硕士生,主要从事资源环境与遥感应用研究。E-mail: 2112101083@e.gzhu.edu.cn
基金资助:
国家自然科学基金项目(41801307);国家自然科学基金项目(42007406);广州市科技计划项目(202102020666)

Spatialization of Carbon Emissions in Guangzhou City by Combining Luojia1-01 Nighttime Light and Urban Functional Zoning Data

LU Yifan(), LIANG Yingran, LU Siyan, XIAO Yue, HE Xiaoyu, LIN Jinyao()

School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China

Received:2021-11-15 Revised:2021-12-01 Online:2022-06-25 Published:2022-08-25
Contact: LIN Jinyao
Supported by:
National Natural Science Foundation of China(41801307);National Natural Science Foundation of China(42007406);Guangzhou Science and Technology Plan Project(202102020666)

摘要/Abstract

摘要：

合理模拟城市内部的碳排放空间分布情况,是制定清晰明确的碳减排政策的重要前提。由于以往相关研究所用数据分辨率较低,且未考虑行业差异,因此所得结果较难精细地反映碳排放空间分布特征。为解决以上不足,本文提出一种更为合理的碳排放空间分布模拟方法。首先利用时间序列法预测2019年广州市各行业碳排放量;然后结合“珞珈一号”夜间灯光及城市功能分区数据,在精细尺度下实现分行业的碳排放空间化;在此基础上进行空间自相关分析,揭示广州市碳排放空间分布规律;最后采用随机森林模型分析影响广州市分行业碳排放的社会经济驱动因素。结果表明：① 广州市碳排放量在2011年后呈缓慢增长趋势,2019年碳排放量达83.12百万吨,其主要贡献来源为交通行业;② 与常用的ODIAC（1 km）、EDGAR（10 km）碳排放产品及基于NPP-VIIRS的碳排放空间化结果（500 m）相比,结合高分辨率（130 m）夜间灯光数据以及城市功能分区实现的碳排放空间化结果可以在更精细的尺度上呈现区域内部的空间碳排放差异;③ 广州市碳排放呈显著的全局空间正相关,形成了以第二和第三产业集中区域为依托的高高聚集区;④ 广州市2019年第二产业碳排放的主要影响因素是一般公共预算收入、第二产业GDP、一般公共预算支出、固定资产投资额;第三产业碳排放的主要影响因素是社会消费品零售额、第三产业GDP、各个行政区总GDP以及人口数量。综上,本研究从城市内部行业结构差异出发,结合高分辨率的夜间灯光数据,展现区域内部的碳排放分布格局,所得结果将有利于相关部门制定精准的碳减排和产业优化升级策略。

关键词: 碳排放, 珞珈一号, 城市功能分区, 空间化, 空间自相关, 随机森林, 影响因素分析, 广州市

Abstract:

A reasonable spatialization of urban carbon emissions is an important prerequisite for formulating clear carbon emission reduction policies. However, previous studies relied heavily on the nighttime light data with coarse spatial resolution and did not consider the huge differences of carbon emissions between various industry sectors. Therefore, the corresponding results cannot accurately reflect the spatial distribution of carbon emissions. To solve the disadvantages of previous methods, this study proposed a more reasonable method for the spatialization of carbon emissions. Firstly, three statistical models were used to estimate the carbon emissions of various industry sectors for Guangzhou in 2019. Next, the spatial distribution of carbon emissions was simulated based on the combined use of Luojia1-01 nighttime light and urban functional zoning data. Based on the spatialization result, both the global and local spatial autocorrelation analyses were carried out to reveal the spatial characteristics of carbon emissions in Guangzhou. Finally, the random forest model was used to investigate the socio-economic driving factors behind the carbon emissions in Guangzhou. The results are summarized as follows: (1) Although the carbon emissions of Guangzhou increased slowly after 2011, the total emission volume still reached 83.12 million tons in 2019, in which the transportation sector played a dominant role; (2) Compared with the commonly-used ODIAC (1 km), EDGAR (10 km) carbon emission products and the carbon emission spatialization results based on NPP-VIIRS (500 m), the result generated by high resolution (130 m) nighttime light and urban functional zoning data can more accurately characterize the spatial differences of carbon emissions; (3) There was a significant positive global spatial autocorrelation of carbon emissions in Guangzhou, resulting in highly concentration areas of secondary and tertiary sectors; (4) The main influencing factors for the secondary sector's carbon emissions were public budget revenue, GDP of the secondary sector, public budget expenditure, and fixed asset investment. In comparison, the major contributors to the tertiary sector's emissions were retail sales of consumer goods, GDP of the tertiary sector, GDP per district, and population. In summary, this study carefully considers the differences in industry structure, and then utilizes the high-resolution nighttime light data to investigate the distribution pattern of carbon emissions. The results will be helpful for policy-makers to formulate reasonable carbon emission reduction and industrial optimization strategies.

Key words: carbon emission, Luojia1-01, urban functional zoning, spatialization, spatial autocorrelation, random forest, influencing factors, Guangzhou City

卢奕帆, 梁颖然, 卢思言, 肖钺, 何小钰, 林锦耀. 结合“珞珈一号”夜间灯光与城市功能分区的广州市碳排放空间分布模拟及其影响因素分析[J]. 地球信息科学学报, 2022, 24(6): 1176-1188.DOI:10.12082/dqxxkx.2022.210610

LU Yifan, LIANG Yingran, LU Siyan, XIAO Yue, HE Xiaoyu, LIN Jinyao. Spatialization of Carbon Emissions in Guangzhou City by Combining Luojia1-01 Nighttime Light and Urban Functional Zoning Data[J]. Journal of Geo-information Science, 2022, 24(6): 1176-1188.DOI:10.12082/dqxxkx.2022.210610

图/表 12

表1

图1

图2

表2

图3

表3

图4

图5

图6

图7

图8

表4

参考文献 36

[1]	邓祥征, 蒋思坚, 刘冰,等. 全球二氧化碳浓度非均匀分布条件下碳排放与升温关系的统计分析[J]. 自然资源学报, 2021, 36(4):934-947.
	[ Deng X Z, Jiang S J, Liu B, et al. Statistical analysis of the relationship between carbon emissions and temperature rise with the spatially heterogenous distribution of carbon dioxide concentration[J]. Journal of Natural Resources, 2021, 36(4):934-947. ] DOI: 10.31497/zrzyxb.20210410 doi: 10.31497/zrzyxb.20210410
[2]	张永年, 潘竟虎. 基于Dmsp/Ols数据的中国碳排放时空模拟与分异格局[J]. 中国环境科学, 2019, 39(4):1436-1446.
	[ Zhang Y N, Pan J H, et al. Spatio-temporal simulation and differentiation pattern of carbon emissions in China based on DMSP/OLS nighttime light data[J]. China Environmental Science, 2019, 39(4):1436-1446. ] DOI: 10.3969/j.issn.1000-6923.2019.04.011 doi: 10.3969/j.issn.1000-6923.2019.04.011
[3]	Liu Z, Guan D, Wei W, et al. Reduced carbon emission estimates from fossil fuel combustion and cement production in China[J]. Nature, 2015, 524(7565):335-338. DOI: 10.1038/nature14677 doi: 10.1038/nature14677
[4]	邓祥征, 丹利, 叶谦,等. 碳排放和减碳的社会经济代价研究进展与方法探究[J]. 地球信息科学学报, 2018, 20(4):405-413. doi: 10.12082/dqxxkx.2018.170590
	[ Deng X Z, Dan L, Ye Q, et al. Methodological framework and research progress on the social and economic cost of carbon emission and reduction[J]. Journal of Geo-information Science, 2018, 20(4):405-413. ] DOI: 10.12082/dqxxkx.2018.170590 doi: 10.12082/dqxxkx.2018.170590
[5]	蔡博峰, 王金南. 基于1km网格的天津市二氧化碳排放研究[J]. 环境科学学报, 2013, 33(6):1655-1664.
	[ Cai B F, Wang J N. CO₂ Emissions of Tianjin Based On 1 Km Grid Dataset[J]. Huanjing Kexue Xuebao / Acta Scientiae Circumstantiae, 2013, 33(6):1655-1664. ] DOI: 10.13671/j.hjkxxb.2013.06.026 doi: 10.13671/j.hjkxxb.2013.06.026
[6]	Chuai X, Feng J. High resolution carbon emissions simulation and spatial heterogeneity analysis based on big data in Nanjing City, China[J]. Science of the Total Environment, 2019, 686(10):828-837. DOI: 10.1016/j.scitotenv.2019.05.138 doi: 10.1016/j.scitotenv.2019.05.138
[7]	Gurney K R, Liang J, Patarasuk R, et al. The vulcan version 3.0 high-resolution fossil fuel CO₂ emissions for the United States[J]. Journal of Geophysical Research: Atmospheres, 2020, 125(19). DOI: 10.1029/2020JD032974 doi: 10.1029/2020JD032974
[8]	Cai M, Shi Y, Ren C. Developing a high-resolution emission inventory tool for low-carbon city management using hybrid method: A pilot test in high-density Hong Kong[J]. Energy and Buildings, 2020, 226:110376. DOI: 10.1016/j.enbuild.2020.110376 doi: 10.1016/j.enbuild.2020.110376
[9]	郭忻怡, 闫庆武, 谭晓悦,等. 基于Dmsp/Ols与Ndvi的江苏省碳排放空间分布模拟[J]. 世界地理研究, 2016, 25(4):102-110.
	[ Guo X, Yan Q, Tan X,et al. Spatial distribution of carbon emissions based on DMSP/OLS nighttime light data and NDVI in Jiangsu province[J]. World Regional Studies, 2016, 25(4):102-11. ] DOI: 10.3969/j.issn.1004-9479.2016.04.012 doi: 10.3969/j.issn.1004-9479.2016.04.012
[10]	武娜, 沈镭, 钟帅. 基于夜间灯光数据的晋陕蒙能源消费碳排放时空格局[J]. 地球信息科学学报, 2019, 21(7):1040-1050. doi: 10.12082/dqxxkx.2019.190010
	[ Wu N, Shen L, Zhong S. Spatio-temporal pattern of carbon emissions based on nightlight data of Shanxi-Shaanxi-Inner Mongolia region of China[J]. Journal of Geo-information Science, 2019, 21(7):1040-1050. ] DOI: 10.12082/dqxxkx.2019.190010 doi: 10.12082/dqxxkx.2019.190010
[11]	余柏蒗, 王丛笑, 宫文康,等. 夜间灯光遥感与城市问题研究:数据、方法、应用和展望[J]. 遥感学报, 2021, 25(1):342-364.
	[ Yu B L, Wang C X, Gong W K, et al. Nighttime light remote sensing and urban studies:Data, methods, applications, and prospects[J]. National Remote Sensing Bulletin, 2021, 25(1):342-364. ] DOI: 10.11834/jrs.20211018 doi: 10.11834/jrs.20211018
[12]	李建豹, 黄贤金, 孙树臣,等. 长三角地区城市土地与能源消费CO₂排放的时空耦合分析[J]. 地理研究, 2019, 38(9):2188-2201. doi: 10.11821/dlyj020180774
	[ Li J B, Huang X J, Sun S C, et al. Spatio-temporal coupling analysis of urban land and carbon dioxide emissions from energy consumption in the Yangtze River Delta region[J]. Geographical Reserch, 2019, 38(9):2188-2201. ] DOI: 10.11821/dlyj020180774 doi: 10.11821/dlyj020180774
[13]	王少剑, 苏泳娴, 赵亚博. 中国城市能源消费碳排放的区域差异、空间溢出效应及影响因素[J]. 地理学报, 2018, 73(3):414-428. doi: 10.11821/dlxb201803003
	[ Wang S J, Su Y X, Zhao Y B. Regional inequality, spatial spillover effects and influencing factors of China's city-level energy-related carbon emissions[J]. Acta Geographica Sinica, 2018, 73(3):414-428. ] DOI: 10.11821/dlxb201803003 doi: 10.11821/dlxb201803003
[14]	Elvidge C D, Baugh K E, Kihn E A, et al. Mapping city lights with nighttime data from the DMSP operational linescan system[J]. Eng & Remote Sens, 1997, 63(6):727-734. DOI: 10.1016/S0924-2716(97)00008-7 doi: 10.1016/S0924-2716(97)00008-7
[15]	Oda T, Maksyutov S. A very high-resolution (1 km×1 km) global fossil fuel CO₂ emission inventory derived using a point source database and satellite observations of nighttime lights[J]. Atmospheric Chemistry and Physics, 2011, 11(2):16307-16344. DOI: 10.5194/acp-11-543-2011 doi: 10.5194/acp-11-543-2011
[16]	Muntean M, Janssens-Maenhout G, Song S, et al. Trend analysis from 1970 to 2008 and model evaluation of EDGARv4 global gridded anthropogenic mercury emissions[J]. Science of the Total Environment, 2014, 494-495(10):337-350. DOI: 10.1016/j.scitotenv.2014.06.014 doi: 10.1016/j.scitotenv.2014.06.014
[17]	苏泳娴, 陈修治, 叶玉瑶,等. 基于夜间灯光数据的中国能源消费碳排放特征及机理[J]. 地理学报, 2013, 68(11):1513-1526.
	[ Su Y X, Chen X Z, Ye Y Y, et al. The characteristics and mechanisms of carbon emissions from energy consumption in China using DMSP/OLS night light imageries[J]. Acta Geographica Sinica, 2013, 68(11):1513-1526. ] DOI: 10.11821/dlxb201311007 doi: 10.11821/dlxb201311007
[18]	Shi K, Yu B, Zhou Y, et al. Spatiotemporal variations of CO₂ emissions and their impact factors in China: A comparative analysis between the provincial and prefectural levels[J]. Applied Energy, 2019, 233-234:170-181. DOI: 10.1016/j.apenergy.2018.10.050 doi: 10.1016/j.apenergy.2018.10.050
[19]	于博, 杨旭, 吴相利. 哈长城市群县域碳排放空间溢出效应及影响因素研究--基于Npp-Viirs夜间灯光数据的实证[J]. 环境科学学报, 2020, 40(2):697-706.
	[ Yu B, Yang X, Wu X L. Study on spatial spillover effects and influencing factors of carbon emissions in county areas of Ha-Chang city group: Evidence from NPP-VIIRS nightlight data[J]. Acta Scientiae Circumstantiae, 2020, 40(2):697-706. ] DOI: 10.13671/j.hjkxxb.2019.0402 doi: 10.13671/j.hjkxxb.2019.0402
[20]	牛亚文, 赵先超, 胡艺觉. 基于Npp-Viirs夜间灯光的长株潭地区县域土地利用碳排放空间分异研究[J]. 环境科学学报, 2021, 41(9):3847-3856.
	[ Niu Y W, Zhao X C, Hu Y J. Spatial variation of carbon emissions from county land use in Chang-Zhu-Tan area based on NPP-VIIRS night light[J]. Acta Scientiae Circumstantiae, 2021, 41(9):3847-3856. ] DOI: 10.13671/j.hjkxxb.2021.0281 doi: 10.13671/j.hjkxxb.2021.0281
[21]	Wang C, Chen Z, Yang C, et al. Analyzing parcel-level relationships between Luojia 1-01 Nighttime Light intensity and artificial surface features across Shanghai, China:A comparison with NPP-VIIRS data[J]. International Journal of Applied Earth Observation and Geoinformation, 2019, 85:101989. DOI: 10.1016/j.jag.2019.101989 doi: 10.1016/j.jag.2019.101989
[22]	Zhang Guo, Xue Y, et al. Evaluating the potential of LJ1-01 Nighttime Light Data for modeling socio-economic parameters[J]. Sensors, 2019, 19(6):1465. DOI: 10.3390/s19061465 doi: 10.3390/s19061465
[23]	吴健生, 牛妍, 彭建,等. 基于DMSP/OLS夜间灯光数据的1995-2009年中国地级市能源消费动态[J]. 地理研究, 2014, 33(4):625-634. doi: 10.11821/dlyj201404003
	[ Wu J S, Niu Y, Peng J, et al. Research on energy consumption dynamic among prefecture-level cities in China based on DMSP/OLS Nighttime Light[J]. Geographical Research, 2014, 33(4):625-634. ] DOI: 10.11821/dlyj201404003 doi: 10.11821/dlyj201404003
[24]	唐建荣, 陈实. 基于随机森林方法的碳足迹及其影响因素研究[J]. 统计与信息论坛, 2013, 28(10):70-74.
	[ Tang J R Chen S. Study on the impact factors of the carbon footprint based on Random Forest[J]. Statistics & Information Forum, 2013, 28(10):70-74. ] DOI: 10.3969/j.issn.1007-3116.2013.10.012 doi: 10.3969/j.issn.1007-3116.2013.10.012
[25]	Gong P, Chen B, Li X, et al. Mapping Essential Urban Land Use Categories in China (EULUC-China): Preliminary Results for 2018[J]. Science Bulletin, 2019, 65(3):182-187. DOI: 10.1016/j.scib.2019.12.007 doi: 10.1016/j.scib.2019.12.007
[26]	李德仁, 张过, 沈欣,等. 珞珈一号01星夜光遥感设计与处理[J]. 遥感学报, 2019, 23(6):1011-1022.
	[ Li D R, Zhang G, Shen X,et al. Design and processing night light remote sensing of LJ-1 01 satellite[J]. Journal of Remote Sensing, 2019, 23(6):1011-1022. ] DOI: 10.11834/jrs.20199327 doi: 10.11834/jrs.20199327
[27]	Doll C H, Muller J P, Elvidge C D. Night-time imagery as a tool for global mapping of socioeconomic parameters and greenhouse gas emissions[J]. Ambio A Journal of the Human Environment, 2000, 29(3):157-162. DOI: 10.1579/0044-7447-29.3.157 doi: 10.1579/0044-7447-29.3.157
[28]	杜海波, 魏伟, 张学渊,等. 黄河流域能源消费碳排放时空格局演变及影响因素--基于DMSP/OLS与NPP/VIIRS夜间灯光数据[J]. 地理研究, 2021, 40(7):2051-2065. doi: 10.11821/dlyj020200646
	[ Du H B, Wei W, Zhang X Y, et al. Spatio-temporal evolution and influencing factors of energy-related carbon emissions in the Yellow River Basin: Based on the DMSP/OLS and NPP/VIIRS nighttime light data[J]. Geographical Research, 2021, 40(7):2051-2065. ] DOI: 10.11821/dlyj020200646 doi: 10.11821/dlyj020200646
[29]	李建豹, 黄贤金, 揣小伟,等. 江苏省人口城镇化与能源消费CO₂排放耦合协调度时空格局及影响因素[J]. 经济地理, 2021, 41(5):57-64.
	[ Li J B, H X J, Chuai X W, et al. Spatial-temporal Pattern and Influencing Factors of Coupling Coordination Degree Between Urbanization of Population and CO2 Emissions of Energy Consumption in Jiangsu Province[J]. Economic Geogaphy, 2021, 41(5):57-64. ] DOI: 10.15957/j.cnki.jjdl.2021.05.007 doi: 10.15957/j.cnki.jjdl.2021.05.007
[30]	张雨欣, 李熙, 宋杨,等. 基于“珞珈一号”夜光遥感影像的粤港澳大湾区城市空间形态分析[J]. 应用科学学报, 2020, 38(3):466-477.
	[ Zhang Y X, Li X, Song Y, et al. Urban Spatial Form Analysis of GBA Based on “LJ1-01” Nighttime Light Remote Sensing Images[J]. Journal of Applied Sciences-Electronics and Information Engineering, 2019, 23(6):1011-1022. ] DOI: 10.3969/j.issn.0255-8297.2020.03.012 doi: 10.3969/j.issn.0255-8297.2020.03.012
[31]	林中立, 徐涵秋, 林丛华,等. 基于珞珈一号夜间灯光数据的福建省人为热通量估算[J/OL]. 遥感学报, 2021.
	[ Lin Z L, Zu H Q, Lin C H, et al. Estimation of anthropogenic heat flux of Fujian Province(China) based on Luojia 1-01 nighttime light data[J/OL]. Journal of Remote Sensing. 2021. ] DOI: 10.11834/jrs.20210295 doi: 10.11834/jrs.20210295
[32]	廖翠萍. 广州市碳排放达峰和“十四五”低碳发展战略路径研究[R]. 广州: 中国能源研究会能源系统工程专业委员会, 2020.
	[ Liao C P Peak carbon emission in Guangzhou and the strategic path of low-carbon development in the 14th Five-year Plan[R]. Guangzhou: Energy System Engineering Committee of China Energy Research Society, 2020. ]
[33]	杨莉, 张雪磊. 江苏居民消费碳排放测度与影响因素研究--基于GTWR模型的实证分析[J]. 生态经济, 2020, 36(5):31-38.
	[ Yang L Zhang X L. Study on Measurement and influencing factors of household carbon emission in Jiangsu Province: An empirical analysis based on GTWR Model[J]. Ecological Economy, 2020, 36(5):31-38. ] doi: 10.1016/S0921-8009(00)00211-1
[34]	范建双, 虞晓芬, 周琳. 南京市土地利用结构碳排放效率增长及其空间相关性[J]. 地理研究, 2018, 37(11):2177-2192. doi: 10.11821/dlyj201811005
	[ Fan J S, Yu X F, Zhou L. Carbon emission efficiency growth of land use structure and its spatial correlation: A case study of Nanjing city e[J]. Geographical Research, 2018, 37(11):2177-2192. ] DOI: 10.11821/dlyj201811005 doi: 10.11821/dlyj201811005
[35]	Breiman L. Random Forests-Random Features[J]. Machine learning, 2001, 45:5-31. DOI: 10.1023/A:1010933404324 doi: 10.1023/A:1010933404324
[36]	邢晓语, 杨秀春, 徐斌,等. 基于随机森林算法的草原地上生物量遥感估算方法研究[J]. 地球信息科学学报, 2021, 23(7):1312-1324. doi: 10.12082/dqxxkx.2021.200605
	[ Xing X Y, Yang X C, Xu B, et al. Remote sensing estimation of grassland aboveground biomass based on random forest[J]. Journal of Geo-information Science, 2021, 23(7):1312-1324. ] DOI: 10.12082/dqxxkx.2021.200605 doi: 10.12082/dqxxkx.2021.200605

数据	时间	来源
城市功能分区^[25]	2019年	清华大学地球系统科学系
“珞珈一号”夜间灯光^[26]	2018年	武汉大学测绘遥感信息工程国家重点实验室
NPP-VIIRS夜间灯光数据	2019年	美国国家海洋和大气管理局
广州市碳排放总量	1997—2017年	中国碳核算数据库
广州市各行业碳排放量比例	2019年	《广州市碳排放达峰和“十四五”低碳发展战略路径研究》报告
ODIAC碳排放产品	2019年	Open-source Data Inventory for Anthropogenic CO₂ Emissions Database for Global Atmospheric Research
EDGAR碳排放产品	2019年
各行政区的社会经济指标	2019	广州市统计局

	居民生活消费	第一产业	第二产业	第三产业（不含交通）	交通
产业碳排放结构/%	15.4	0.6	41.2	5.8	37.0
分产业碳排放量/百万吨	12.8003	0.4987	34.2450	4.7943	30.7806

行业分区	灯光碳排放系数（t碳/单位DN值）
居民生活消费	0.45×10^-4
第二产业	1.36×10^-4
第三产业（不含交通）	0.80×10^-4
交通	5.20×10^-4

影响因素	第二产业碳排放影响因素重要性（排序）	第三产业碳排放影响因素重要性（排序）
一般公共预算收入	4.32（1）	0.55（8）
一般公共预算支出	2.64（3）	1.41（5）
总GDP	0.79（8）	2.07（3）
产业GDP	3.74（2）	2.69（2）
城镇化率	1.40（5）	0.50（9）
固定资产投资额	2.34（4）	1.07（6）
消费品零售总额	1.40（6）	4.36（1）
常住人口密度	1.10（7）	0.82（7）
常住人口数量	0.40（9）	1.76（4）

结合“珞珈一号”夜间灯光与城市功能分区的广州市碳排放空间分布模拟及其影响因素分析

Spatialization of Carbon Emissions in Guangzhou City by Combining Luojia1-01 Nighttime Light and Urban Functional Zoning Data

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 12

参考文献 36

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	罗永臻, 董春, 张玉. 人口空间化适宜格网评价方法研究[J]. 地球信息科学学报, 2023, 25(5): 896-908.
[2]	林炫歆, 肖桂荣, 周侯伯. 顾及土地利用动态变化的滑坡易发性评估方法[J]. 地球信息科学学报, 2023, 25(5): 953-966.
[3]	封雅静, 翟亮, 桑会勇, 成思远. 基于精细单元碳收支核算及变化分类方法[J]. 地球信息科学学报, 2023, 25(3): 468-478.
[4]	秦肖伟, 程博, 杨志平, 李林, 董文, 张新, 杨树文, 靳宗义, 薛庆. 基于时序遥感影像的西南山区地块尺度作物类型识别[J]. 地球信息科学学报, 2023, 25(3): 654-668.
[5]	黄帅元, 董有福, 李海鹏. 黄土高原区SRTM1 DEM高程误差校正模型构建及对比分析[J]. 地球信息科学学报, 2023, 25(3): 669-681.
[6]	潘雨飘, 赵翔, 王静, 张亦清, 刘耀林. 基于SMOTE-RF算法的村庄发展类型识别方法研究[J]. 地球信息科学学报, 2023, 25(1): 163-176.
[7]	杨洋, 邵哲平, 赵强, 潘家财, 胡雨, 梅强. 基于厦门港的海上交通事故地理空间分布及风险预测研究[J]. 地球信息科学学报, 2022, 24(9): 1676-1687.
[8]	吴亚楠, 郭长恩, 于东平, 段爱民, 刘玉, 董士伟, 单东方, 吴耐明, 李西灿. 基于不确定性分析的遥感分类空间分层及评估方法[J]. 地球信息科学学报, 2022, 24(9): 1803-1816.
[9]	聂祥琴, 陈瀚阅, 牛铮, 张黎明, 刘炜, 邢世和, 范协裕, 李家国. 基于时序影像的农业活动因子提取与闽西耕地SOC数字制图[J]. 地球信息科学学报, 2022, 24(9): 1835-1852.
[10]	高位, 涂伟, 李明晓, 方碧宸, 陈栋胜, 黄正东, 贺彪. 基于影响因子动态更新的都市圈国土-人口协同模拟[J]. 地球信息科学学报, 2022, 24(8): 1502-1511.
[11]	裴泽华, 葛淼, 李浩, 何进伟, 王聪霞. 基于随机森林模型的中国中老年人群HDL-C环境影响因素研究[J]. 地球信息科学学报, 2022, 24(7): 1286-1300.
[12]	郭逸飞, 吴田军, 骆剑承, 石含宁, 郜丽静. 基于不确定性迭代优化的山地植被遥感制图[J]. 地球信息科学学报, 2022, 24(7): 1406-1419.
[13]	高雪梅, 杨续超, 陈柏儒, 林琳. 基于随机森林模型的环渤海地区人口空间化模拟[J]. 地球信息科学学报, 2022, 24(6): 1150-1162.
[14]	耿佳辰, 沈石, 程昌秀. “十三五”时期黄河流域PM_2.5时空分布规律及多尺度社会经济影响机制分析[J]. 地球信息科学学报, 2022, 24(6): 1163-1175.
[15]	张永凯, 杨春月, 张晚军, 毕潇梅. 黄河流域人口预期寿命的时空演化及影响因素分析[J]. 地球信息科学学报, 2022, 24(5): 902-913.