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()   

  1. 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()   

  1. 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
  • 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:

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