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Differential Spatiotemporal Patterns of CO2 Emissions in Eastern China's Urban Agglomerations from NPP/VIIRS Nighttime Light Data Based on a Neural Network Algorithm

文献类型：期刊论文

作者Zhou, Lei1,2; Song, Jun1; Chi, Yonggang1; Yu, Quanzhou3
刊名REMOTE SENSING
出版日期2023
卷号15期号:2页码:16
关键词carbon emission energy conservation genetic algorithm nighttime light data urban agglomerations
DOI10.3390/rs15020404
通讯作者Chi, Yonggang(chiyonggang@zjnu.cn)
英文摘要Urban agglomerations, such as Beijing-Tianjin-Hebei Region, Yangtze River Delta and Pearl River Delta, are the key regions for energy conservation, carbon emission reduction and low-carbon development in China. However, spatiotemporal patterns of CO2 emissions at fine scale in these major urban agglomerations are not well documented. In this study, a back propagation neural network based on genetic algorithm optimization (GABP) coupled with NPP/VIIRS nighttime light datasets was established to estimate the CO2 emissions of China's three major urban agglomerations at 500 m resolution from 2014 to 2019. The results showed that spatial patterns of CO2 emissions presented three-core distribution in the Beijing-Tianjin-Hebei Region, multiple-core distribution in the Yangtze River Delta, and null-core distribution in the Pearl River Delta. Temporal patterns of CO2 emissions showed upward trends in 28.74-43.99% of the total areas while downward trends were shown in 13.47-15.43% of the total areas in three urban agglomerations. The total amount of CO2 emissions in urban areas was largest among urban circles, followed by first-level urban circles and second-level urban circles. The profiles of CO2 emissions along urbanization gradients featured high peaks and wide ranges in large cities, and low peaks and narrow ranges in small cities. Population density primarily impacted the spatial pattern of CO2 emissions among urban agglomerations, followed by terrain slope. These findings suggested that differences in urban agglomerations should be taken into consideration in formulating emission reduction policies.
WOS关键词PEARL RIVER DELTA ; RESIDENTIAL ENERGY-CONSUMPTION ; ELECTRIC-POWER CONSUMPTION ; CARBON-DIOXIDE EMISSIONS ; DRIVING FORCES ; IMPACT FACTORS ; LEVEL ; CITIES ; POPULATION ; DYNAMICS
资助项目National Natural Science Foundation of China[41871084] ; National Key Research and Development Program of China[2017YFB0504000] ; Soft Science Research Program of Zhejiang Provincial Department of Science and Technology[2022C35095] ; Jinhua Science and Technology Research Program[2021-4-340] ; Jinhua Science and Technology Research Program[2020-4-184] ; Self-Design Project in Zhejiang Normal University[2021ZS07]
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:000918909100001
出版者MDPI
资助机构National Natural Science Foundation of China ; National Key Research and Development Program of China ; Soft Science Research Program of Zhejiang Provincial Department of Science and Technology ; Jinhua Science and Technology Research Program ; Self-Design Project in Zhejiang Normal University
源URL[http://ir.igsnrr.ac.cn/handle/311030/189410]  
专题中国科学院地理科学与资源研究所
通讯作者Chi, Yonggang
作者单位1.Zhejiang Normal Univ, Coll Geog & Environm Sci, Jinhua 321004, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modelling, Beijing 100101, Peoples R China
3.Liaocheng Univ, Sch Geog & Environm, Liaocheng 252059, Peoples R China
推荐引用方式
GB/T 7714		 Zhou, Lei,Song, Jun,Chi, Yonggang,et al. Differential Spatiotemporal Patterns of CO2 Emissions in Eastern China's Urban Agglomerations from NPP/VIIRS Nighttime Light Data Based on a Neural Network Algorithm[J]. REMOTE SENSING,2023,15(2):16.
APA Zhou, Lei,Song, Jun,Chi, Yonggang,&Yu, Quanzhou.(2023).Differential Spatiotemporal Patterns of CO2 Emissions in Eastern China's Urban Agglomerations from NPP/VIIRS Nighttime Light Data Based on a Neural Network Algorithm.REMOTE SENSING,15(2),16.
MLA Zhou, Lei,et al."Differential Spatiotemporal Patterns of CO2 Emissions in Eastern China's Urban Agglomerations from NPP/VIIRS Nighttime Light Data Based on a Neural Network Algorithm".REMOTE SENSING 15.2(2023):16.

入库方式： OAI收割

来源：地理科学与资源研究所

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